Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics
Impact Factor:
5.0 (2022);
5-Year Impact Factor:
5.6 (2022)
Latest Articles
FFEDet: Fine-Grained Feature Enhancement for Small Object Detection
Remote Sens. 2024, 16(11), 2003; https://doi.org/10.3390/rs16112003 (registering DOI) - 2 Jun 2024
Abstract
Small object detection poses significant challenges in the realm of general object detection, primarily due to complex backgrounds and other instances interfering with the expression of features. This research introduces an uncomplicated and efficient algorithm that addresses the limitations of small object detection.
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Small object detection poses significant challenges in the realm of general object detection, primarily due to complex backgrounds and other instances interfering with the expression of features. This research introduces an uncomplicated and efficient algorithm that addresses the limitations of small object detection. Firstly, we propose an efficient cross-scale feature fusion attention module called ECFA, which effectively utilizes attention mechanisms to emphasize relevant features across adjacent scales and suppress irrelevant noise, tackling issues of feature redundancy and insufficient representation of small objects. Secondly, we design a highly efficient convolutional module named SEConv, which reduces computational redundancy while providing a multi-scale receptive field to improve feature learning. Additionally, we develop a novel dynamic focus sample weighting function called DFSLoss, which allows the model to focus on learning from both normal and challenging samples, effectively addressing the problem of imbalanced difficulty levels among samples. Moreover, we introduce Wise-IoU to address the impact of poor-quality examples on model convergence. We extensively conduct experiments on four publicly available datasets to showcase the exceptional performance of our method in comparison to state-of-the-art object detectors.
Full article
(This article belongs to the Special Issue Semantic Segmentation of High-Resolution Remote Sensing Images with Advanced Deep Learning Techniques)
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Open AccessArticle
Assessment of C-Band Polarimetric Radar for the Detection of Diesel Fuel in Newly Formed Sea Ice
by
Leah Hicks, Mahdi Zabihi Mayvan, Elvis Asihene, Durell S. Desmond, Katarzyna Polcwiartek, Gary A. Stern and Dustin Isleifson
Remote Sens. 2024, 16(11), 2002; https://doi.org/10.3390/rs16112002 (registering DOI) - 2 Jun 2024
Abstract
There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an
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There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group.
Full article
(This article belongs to the Section Environmental Remote Sensing)
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Open AccessArticle
Quantitative Assessment of Volcanic Thermal Activity from Space Using an Isolation Forest Machine Learning Algorithm
by
Claudia Corradino, Arianna Beatrice Malaguti, Micheal S. Ramsey and Ciro Del Negro
Remote Sens. 2024, 16(11), 2001; https://doi.org/10.3390/rs16112001 (registering DOI) - 1 Jun 2024
Abstract
Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and
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Understanding the dynamics of volcanic activity is crucial for volcano observatories in their efforts to forecast volcanic hazards. Satellite imager data hold promise in offering crucial insights into the thermal behavior of active volcanoes worldwide, facilitating the assessment of volcanic activity levels and identifying significant changes during periods of volcano unrest. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard NASA’s Terra and Aqua satellites, provides invaluable data with high temporal and spectral resolution, enabling comprehensive thermal monitoring of eruptive activity. The accuracy of volcanic activity characterization depends on the quality of models used to relate the relationship between volcanic phenomena and target variables such as temperature. Under these circumstances, machine learning (ML) techniques such as decision trees can be employed to develop reliable models without necessarily offering any particular or explicit insights. Here, we present a ML approach for quantifying volcanic thermal activity levels in near real time using thermal infrared satellite data. We develop an unsupervised Isolation Forest machine learning algorithm, fully implemented in Google Colab using Google Earth Engine (GEE) which utilizes MODIS Land Surface Temperature (LST) data to automatically retrieve information on the thermal state of volcanoes. We evaluate the algorithm on various volcanoes worldwide characterized by different levels of volcanic activity.
Full article
(This article belongs to the Special Issue Technologies for Forecasting Volcanic Hazards: From Remote Sensing to Modeling)
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Open AccessArticle
Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty
by
Yuanfan Zhang, Zhixi Nie, Zhenjie Wang, Guohong Zhang and Xinjian Shan
Remote Sens. 2024, 16(11), 2000; https://doi.org/10.3390/rs16112000 (registering DOI) - 1 Jun 2024
Abstract
A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline
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A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter.
Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
Open AccessCommunication
Universal Software Only Radar with All Waveforms Simultaneously on a Single Platform
by
Vitali Kozlov, Anton Kharchevskii, Eran Rebenshtok, Vjaceslavs Bobrovs, Toms Salgals and Pavel Ginzburg
Remote Sens. 2024, 16(11), 1999; https://doi.org/10.3390/rs16111999 (registering DOI) - 1 Jun 2024
Abstract
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Abstract: While software-defined radars can switch their transmitted waveform on the go, they cannot transmit all waveforms at the same time, meaning they must balance the advantages and drawbacks of each configuration. Here, we propose theoretically and demonstrate experimentally the universal radar, which
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Abstract: While software-defined radars can switch their transmitted waveform on the go, they cannot transmit all waveforms at the same time, meaning they must balance the advantages and drawbacks of each configuration. Here, we propose theoretically and demonstrate experimentally the universal radar, which can apply the desired waveform in the post-processing stage after the physical measurement has been performed. This method also allows using a single measurement of a scene to design and test any other radar in complex scenarios without having to take it to the field. The method is based on post-processing the frequency response measured by a synthetically broadband stepped-frequency continuous wave radar, such as a vector network analyzer. An algorithm for overcoming distortions due to moving targets is derived as well. This approach not only provides an ultra-wideband software-only defined radar, but it also enables the acquired data from any measured site to be used for the design and analysis of almost any other future radar system, significantly cutting the time and cost of new developments. The method suggests the creation of radar raw data repositories that can be shared across diversely different radar platforms.
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Open AccessArticle
Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)
by
Angelo Odetti, Gabriele Bruzzone, Roberta Ferretti, Simona Aracri, Federico Carotenuto, Carolina Vagnoli, Alessandro Zaldei and Ivan Scagnetto
Remote Sens. 2024, 16(11), 1998; https://doi.org/10.3390/rs16111998 (registering DOI) - 1 Jun 2024
Abstract
This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and
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This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.
Full article
(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III)
Open AccessArticle
Application of Multi-Temporal and Multisource Satellite Imagery in the Study of Irrigated Landscapes in Arid Climates
by
Nazarij Buławka and Hector A. Orengo
Remote Sens. 2024, 16(11), 1997; https://doi.org/10.3390/rs16111997 (registering DOI) - 31 May 2024
Abstract
The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The
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The study of ancient irrigation is crucial in the archaeological research of arid regions. It covers a wide range of topics, with the Near East being the focus for decades. However, political instability and limited data have posed challenges to these studies. The primary objective is to establish a standardised method applicable to different arid environments using the Google Earth Engine platform, considering local relief of terrain and seasonal differences in vegetation. This study integrates multispectral data from LANDSAT 5, Sentinel-2, SAR imagery from Sentinel 1, and TanDEM-X (12 m and 30 m) DSMs. Using these datasets, calculations of selected vegetation indices such as the SMTVI and NDVSI, spectral decomposition methods such as TCT and PCA, and topography-based methods such as the MSRM contribute to a comprehensive understanding of landscape irrigation. This paper investigates the influence of modern environmental conditions on the visibility of features like levees and palaeo-channels by testing different methods and parameters. This study aims to identify the most effective approach for each case study and explore the possibility of applying a consistent method across all areas. Optimal results are achieved by combining several methods, adjusting seasonal parameters, and conducting a comparative analysis of visible features.
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Open AccessArticle
Trends of Key Greenhouse Gases as Measured in 2009–2022 at the FTIR Station of St. Petersburg State University
by
Maria Makarova, Anatoly Poberovskii, Alexander Polyakov, Khamud H. Imkhasin, Dmitry Ionov, Boris Makarov, Vladimir Kostsov, Stefani Foka and Evgeny Abakumov
Remote Sens. 2024, 16(11), 1996; https://doi.org/10.3390/rs16111996 (registering DOI) - 31 May 2024
Abstract
Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always
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Key long-lived greenhouse gases (CO2, CH4, and N2O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO2, CH4, and N2O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO2, CH4, and N2O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr−1 for CO2; (0.46 ± 0.02) % yr−1 for CH4; (0.28 ± 0.01) % yr−1 for N2O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH4 and N2O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory.
Full article
(This article belongs to the Special Issue Advances in Remote Sensing and Atmospheric Optics)
Open AccessArticle
Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index
by
Ziwen Wang, Chuanmao Zhang, Lixin Gao, Chengzhi Fan, Xuexin Xu, Fangzhao Zhang, Yiming Zhou, Fangpeng Niu and Zhenhai Li
Remote Sens. 2024, 16(11), 1995; https://doi.org/10.3390/rs16111995 - 31 May 2024
Abstract
Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The
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Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R² of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R² of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting.
Full article
(This article belongs to the Special Issue Recent Progress in UAV-AI Remote Sensing II)
Open AccessArticle
Spatiotemporal Variability of Gross Primary Productivity in Türkiye: A Multi-Source and Multi-Method Assessment
by
Eyyup Ensar Başakın, Paul C. Stoy, Mehmet Cüneyd Demirel and Quoc Bao Pham
Remote Sens. 2024, 16(11), 1994; https://doi.org/10.3390/rs16111994 - 31 May 2024
Abstract
We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Türkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal–Wallis and Mann–Whitney U methods, and long-term trends were analyzed
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We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Türkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal–Wallis and Mann–Whitney U methods, and long-term trends were analyzed using Modified Mann–Kendall (MMK), innovative trend analysis (ITA), and empirical mode decomposition (EMD). Our results show that at least one GPP product significantly differs from the others over the seven geographic regions of Türkiye (χ2 values of 50.8, 21.9, 76.9, 42.6, 149, 34.5, and 168; p < 0.05), and trend analyses reveal a significant increase in GPP from all satellite-based products over the latter half of the study period. Throughout the year, the average number of months in which each dataset showed significant increases across all study regions are 6.7, 8.1, 5.9, 9.6, and 8.7 for MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2, respectively. The ITA and EMD methods provided additional insight into the MMK test in both visualizing and detecting trends due to their graphical techniques. Overall, the GPP products investigated here suggest ‘greening’ for Türkiye, consistent with the findings from global studies, but the use of different statistical approaches and satellite-based GPP estimates creates different interpretations of how these trends have emerged. Ground stations, such as eddy covariance towers, can help further improve our understanding of the carbon cycle across the diverse ecosystem of Türkiye.
Full article
(This article belongs to the Special Issue Remote Sensing of Carbon Fluxes and Stocks II)
Open AccessArticle
DAMF-Net: Unsupervised Domain-Adaptive Multimodal Feature Fusion Method for Partial Point Cloud Registration
by
Haixia Zhao, Jiaqi Sun and Bin Dong
Remote Sens. 2024, 16(11), 1993; https://doi.org/10.3390/rs16111993 - 31 May 2024
Abstract
Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a large number of repetitive planar structures, the point-only based methods struggle to extract distinctive features from
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Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a large number of repetitive planar structures, the point-only based methods struggle to extract distinctive features from the similar structures, which greatly limits the accuracy of registration. Moreover, the deep learning-based approaches achieve commendable results on public datasets, but they face challenges in generalizing to unseen few-shot datasets with significant domain differences from the training data, and that is especially common in industrial applications where samples are generally scarce. Moreover, existing registration methods can achieve high accuracy on complete point clouds. However, for partial point cloud registration, many methods are incapable of accurately identifying correspondences, making it challenging to estimate precise rigid transformations. This paper introduces a domain-adaptive multimodal feature fusion method for partial point cloud registration in an unsupervised manner, named DAMF-Net, that significantly addresses registration challenges in scenes dominated by repetitive planar structures, and it can generalize well-trained networks on public datasets to unseen few-shot datasets. Specifically, we first introduce a point-guided two-stage multimodal feature fusion module that utilizes the geometric information contained in point clouds to guide the texture information in images for preliminary and supplementary feature fusion. Secondly, we incorporate a gradient-inverse domain-aware module to construct a domain classifier in a generative adversarial manner, weakening the feature extractor’s ability to distinguish between source and target domain samples, thereby achieving generalization across different domains. Experiments on a public dataset and our industrial components dataset demonstrate that our method improves the registration accuracy in specific scenarios with numerous repetitive planar structures and achieves high accuracy on unseen few-shot datasets, compared with the results of state-of-the-art traditional and deep learning-based point cloud registration methods.
Full article
(This article belongs to the Section Remote Sensing Image Processing)
Open AccessArticle
Blind Edge-Retention Indicator for Assessing the Quality of Filtered (Pol)SAR Images Based on a Ratio Gradient Operator and Confidence Interval Estimation
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Xiaoshuang Ma, Le Li and Gang Wang
Remote Sens. 2024, 16(11), 1992; https://doi.org/10.3390/rs16111992 - 31 May 2024
Abstract
Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well
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Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator.
Full article
(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)
Open AccessArticle
An Improved Approach to Estimate Stocking Rate and Carrying Capacity Based on Remotely Sensed Phenology Timings
by
Yan Shi, Gary Brierley, George L. W. Perry, Jay Gao, Xilai Li, Alexander V. Prishchepov, Jiexia Li and Meiqin Han
Remote Sens. 2024, 16(11), 1991; https://doi.org/10.3390/rs16111991 - 31 May 2024
Abstract
Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGB
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Accurate estimation of livestock carrying capacity (LCC) and implementation of an appropriate actual stocking rate (ASR) are key to the sustainable management of grazing adapted alpine grassland ecosystems. The reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here, we demonstrate the limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in an effort to support more accurate and reliable estimation of LCC and ASR. The results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. The findings from a smaller-scale study that incorporate phenology timings into the estimation of annual aboveground biomass reveal that summer pastures in Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000 to 2005. The methods proposed can be extended to map grassland grazing pressure by predicting the LCC and tracking the ASR, thereby improving sustainable resource use in alpine grasslands.
Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Phenology II)
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Open AccessReview
Annual Review of In Situ Observations of Tropical Cyclone–Ocean Interaction in the Western North Pacific during 2023
by
Hailun He, Ruizhen Tian, Xinyan Lyu, Zheng Ling, Jia Sun and Anzhou Cao
Remote Sens. 2024, 16(11), 1990; https://doi.org/10.3390/rs16111990 - 31 May 2024
Abstract
We present a review of in situ observations regarding the interactions between tropical cyclones and the ocean in the western North Pacific for the year 2023. A total of at least 13 tropical cyclones occurred during this period. According to the Japan Meteorological
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We present a review of in situ observations regarding the interactions between tropical cyclones and the ocean in the western North Pacific for the year 2023. A total of at least 13 tropical cyclones occurred during this period. According to the Japan Meteorological Agency, Typhoon Mawar recorded the yearly minimum pressure at 900 hPar. On average, each tropical cyclone captured 7.4 surface drifters and 25.2 Argo floats when the search radius is 300 km. During Guchol, the maximum in situ Lagrangian current reached 1.23 m/s, with sustained wind speeds of the tropical cyclone up to 31.7 m/s and a relative position of 174 km. Additionally, several Argo floats were active during tropical cyclones, with maximum sea surface temperature cooling reaching 0.66 °C. This annual review provides a comprehensive summary of the current state of in situ observations regarding tropical cyclone–ocean interaction. These findings serve as valuable references for both scientific research and operational forecasting.
Full article
(This article belongs to the Special Issue Advances in Oceanic Dynamics by SAR and Numeric Model in Tropical Cyclone)
Open AccessArticle
An Attention-Guided Complex-Valued Transformer for Intra-Pulse Retransmission Interference Suppression
by
Yifan Wang, Yibing Li, Zitao Zhou, Gang Yu and Yingsong Li
Remote Sens. 2024, 16(11), 1989; https://doi.org/10.3390/rs16111989 - 31 May 2024
Abstract
With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar detection tasks, particularly in modern battlefields. This paper proposes an attention-guided complex-valued transformer (AGCT)
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With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar detection tasks, particularly in modern battlefields. This paper proposes an attention-guided complex-valued transformer (AGCT) as a solution. First, the encoder maps the received signal contaminated by interference and noise into a high-dimensional space. Then, the dilated convolution block (DCB) group and attention block (AB) group in the mask estimator extract the delicate multi-scale features and large-scale features of the interference, respectively, to obtain a multidimensional space mask. Finally, the decoder restores interference to the time domain and outputs the estimated target echo using residual learning. Considering the characteristics of intra-pulse interference, we introduced the energy attention block (EAB) at the end of the DCBs and the ABs within our network. This addition ensures a heightened focus on extracting interference features. Furthermore, we implemented a curriculum learning strategy during the network training. This approach gradually acclimates the network to fit different types of retransmission interference, starting from simpler to more complex scenarios. Our extensive experiments, conducted under various conditions, have provided compelling evidence of the AGCT’s superior performance. Compared to the comparative network, the AGCT’s advantages are particularly pronounced under more harsh conditions, demonstrating its robustness and effectiveness.
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Open AccessArticle
Multi-Scale Object Detection in Remote Sensing Images Based on Feature Interaction and Gaussian Distribution
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Ruixing Yu, Haixing Cai, Boyu Zhang and Tao Feng
Remote Sens. 2024, 16(11), 1988; https://doi.org/10.3390/rs16111988 - 31 May 2024
Abstract
Remote sensing images are usually obtained from high-altitude observation. The spatial resolution of the images varies greatly and there are scale differences both between and within object classes, resulting in a diversified distribution of object scales. In order to solve these problems, we
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Remote sensing images are usually obtained from high-altitude observation. The spatial resolution of the images varies greatly and there are scale differences both between and within object classes, resulting in a diversified distribution of object scales. In order to solve these problems, we propose a novel object detection algorithm that maintains adaptability to multi-scale object detection based on feature interaction and Gaussian distribution in remote sensing images. The proposed multi-scale feature interaction model constructs feature interaction modules in the feature layer and spatial domain and combines them to fully utilize the spatial and semantic information of multi-level features. The proposed regression loss algorithm based on Gaussian distribution takes the normalized generalized Jensen–Shannon divergence with Gaussian angle loss as the regression loss function to ensure the scale invariance of the model. The experimental results demonstrate that our method achieves 77.29% mAP on the DOTA-v1.0 dataset and 97.95% mAP on the HRSC2016 dataset, which are, respectively, 1.12% and 1.41% higher than that of the baseline. These experimental results indicate the effectiveness of our method for object detection in remote sensing images.
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(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)
Open AccessArticle
Geocomplexity Statistical Indicator to Enhance Multiclass Semantic Segmentation of Remotely Sensed Data with Less Sampling Bias
by
Wei He, Lianfa Li and Xilin Gao
Remote Sens. 2024, 16(11), 1987; https://doi.org/10.3390/rs16111987 - 31 May 2024
Abstract
Challenges in enhancing the multiclass segmentation of remotely sensed data include expensive and scarce labeled samples, complex geo-surface scenes, and resulting biases. The intricate nature of geographical surfaces, comprising varying elements and features, introduces significant complexity to the task of segmentation. The limited
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Challenges in enhancing the multiclass segmentation of remotely sensed data include expensive and scarce labeled samples, complex geo-surface scenes, and resulting biases. The intricate nature of geographical surfaces, comprising varying elements and features, introduces significant complexity to the task of segmentation. The limited label data used to train segmentation models may exhibit biases due to imbalances or the inadequate representation of certain surface types or features. For applications like land use/cover monitoring, the assumption of evenly distributed simple random sampling may be not satisfied due to spatial stratified heterogeneity, introducing biases that can adversely impact the model’s ability to generalize effectively across diverse geographical areas. We introduced two statistical indicators to encode the complexity of geo-features under multiclass scenes and designed a corresponding optimal sampling scheme to select representative samples to reduce sampling bias during machine learning model training, especially that of deep learning models. The results of the complexity scores showed that the entropy-based and gray-based indicators effectively detected the complexity from geo-surface scenes: the entropy-based indicator was sensitive to the boundaries of different classes and the contours of geographical objects, while the Moran’s I indicator had a better performance in identifying the spatial structure information of geographical objects in remote sensing images. According to the complexity scores, the optimal sampling methods appropriately adapted the distribution of the training samples to the geo-context and enhanced their representativeness relative to the population. The single-score optimal sampling method achieved the highest improvement in DeepLab-V3 (increasing pixel accuracy by 0.3% and MIoU by 5.5%), and the multi-score optimal sampling method achieved the highest improvement in SegFormer (increasing ACC by 0.2% and MIoU by 2.4%). These findings carry significant implications for quantifying the complexity of geo-surface scenes and hence can enhance the semantic segmentation of high-resolution remote sensing images with less sampling bias.
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(This article belongs to the Section AI Remote Sensing)
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Research on the Carbon Sequestration Capacity of Forest Ecological Network Topological Features and Network Optimization Based on Modification Recognition in the Yellow River Basin Mining Area: A Case Study of Jincheng City
by
Maolin Li, Qiang Yu, Chenglong Xu, Jikai Zhao, Yufan Zeng, Yu Wang and Yilin Liu
Remote Sens. 2024, 16(11), 1986; https://doi.org/10.3390/rs16111986 - 31 May 2024
Abstract
Forests are vital for terrestrial ecosystems, providing crucial functions like carbon sequestration and water conservation. In the Yellow River Basin, where 70% of forest coverage is concentrated in the middle reaches encompassing Sichuan, Shaanxi, and Shanxi provinces, there exists significant potential for coal
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Forests are vital for terrestrial ecosystems, providing crucial functions like carbon sequestration and water conservation. In the Yellow River Basin, where 70% of forest coverage is concentrated in the middle reaches encompassing Sichuan, Shaanxi, and Shanxi provinces, there exists significant potential for coal production, with nine planned coal bases. This study centered on Jincheng City, Shanxi Province, a representative coal mining area in the Yellow River Basin, and combined the MSPA analysis method and MCR model to generate the five-period forest ecological network of Jincheng City from 1985 to 2022 under the background of coal mining and calculate the degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality; the correlation between the four centralities and carbon sequestration ability is further explored. Simultaneously, employing the RAND-ESU algorithm for motif identification within forest ecological networks, this study integrates the ecological policies of the research area with the specific conditions of the coal mining region to optimize the forest ecological network in Jincheng City. Findings reveal the following. (1) Forest ecological spatial networks: Forest ecological networks exhibit robust overall ecological connectivity in the study area, with potential ecological corridors spanning the region. However, certain areas with high ecological resistance hinder connectivity between key forest ecological nodes under the background of coal mining. (2) Correlation between topological indices and carbon sequestration ecological services: From 1985 to 2022, the carbon sequestration capacity of Jincheng City’s forest source areas increased year by year, and significant positive correlations were observed between degree centrality, betweenness centrality, eigenvector centrality with carbon sequestration ecological services, indicating a strengthening trend over time. (3) Motif Recognition and Ecological Network Optimization: During the study, four types of motifs were identified in the forest ecological network of Jincheng City based on the number of nodes and their connections using the RAND-ESU network motif algorithm. These motifs are 3a, 4a, 4b, and 4d (where the number represents the number of nodes and the letter represents the connection type). Among these, motifs 3a and 4b play a crucial role. Based on these motifs and practical considerations, network optimization was performed on the existing ecological source areas to enhance the robustness of the forest ecological network.
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Near-Surface Dispersion and Current Observations Using Dye, Drifters, and HF Radar in Coastal Waters
by
Keunyong Kim, Hong Thi My Tran, Kyu-Min Song, Yeong Baek Son, Young-Gyu Park, Joo-Hyung Ryu, Geun-Ho Kwak and Jun Myoung Choi
Remote Sens. 2024, 16(11), 1985; https://doi.org/10.3390/rs16111985 - 31 May 2024
Abstract
This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in situ sampling, and HF radar. The aim is to deepen
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This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in situ sampling, and HF radar. The aim is to deepen our understanding of surface currents’ impact on contaminant dispersion, thereby improving predictive models for managing environmental incidents such as pollutant releases. Rhodamine WT dye, chosen for its significant fluorescent properties and detectability, along with drifter data, allowed us to investigate the dynamics of near-surface physical phenomena such as the Ekman current, Stokes drift, and wind-driven currents. Our research emphasizes the importance of integrating scalar tracers and Lagrangian markers in experimental designs, revealing differential dispersion behaviors due to near-surface vertical shear caused by the Ekman current and Stokes drift. During slow-current conditions, the elongation direction of the dye patch aligned well with the direction of a depth-averaged Ekman spiral, or Ekman transport. Analytical calculations of vertical shear, based on the Ekman current and Stokes drift, closely matched those derived from tracer observations. Over a 7 h experiment, the vertical diffusivity near the surface was first observed at the early stages of scalar mixing, with a value of , and the horizontal eddy diffusivity of the dye patch and drifters reached the order of 1 at a 1000 length scale. Particle tracking models demonstrate that while HF radar currents can effectively predict the trajectories of tracers near the surface, incorporating near-surface currents, including the Ekman current, Stokes drift, and windage, is essential for a more accurate prediction of the fate of surface floats.
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(This article belongs to the Special Issue Remote Sensing of Ocean Surface Currents: Measurement, Validation and Applications)
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Open AccessArticle
The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis
by
Jie Chen, Yankun Wang, Jingzhe Wang, Yinghui Zhang, Yue Xu, Ou Yang, Rui Zhang, Jing Wang, Zhensheng Wang, Feidong Lu and Zhongwen Hu
Remote Sens. 2024, 16(11), 1984; https://doi.org/10.3390/rs16111984 - 31 May 2024
Abstract
The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar
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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.
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(This article belongs to the Topic Advances in Multi-Scale Geographic Environmental Monitoring: Theory, Methodology and Applications)
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