IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"Adaptive Pixel-Level and Superpixel-Level Feature Fusion Transformer for Hyperspectral Image Classification","authors":"Wei Huang;Dazhan Zhou;Le Sun;Qiqiang Chen;Junru Yin","doi":"10.1109/JSTARS.2024.3455561","DOIUrl":"10.1109/JSTARS.2024.3455561","url":null,"abstract":"Significant progress has been achieved in hyperspectral image (HSI) classification research through the application of the transformer blocks. Despite transformers possess strong long-range dependence modeling capabilities, they primarily extract nonlocal information from patches and often fail to fully capture global information, leading to incomplete spectral-spatial feature extraction. However, graph convolutional networks (GCNs) can effectively extract features from the global structure. This article proposes an adaptive pixel-level and superpixel-level feature fusion transformer (APSFFT). The network comprises two branches: one is the convolutional neural networks (CNNs) and transformer networks (CNTN), and the other is the GCNs and transformer networks (GNTN). These branches are designed to extract pixel-level and superpixel-level feature information from HSI, respectively. CNTN leverages the strengths of CNNs in extracting spectral–spatial information, combined with the transformer network's ability to establish long-range dependencies based on self-attention (SA). The GNTN fully extracts superpixel-level features while establishing long-range dependencies. To adaptively fuse the features from these two branches, an adaptive cross-token attention fusion (ACTAF) encoder is utilized. The ACTAF encoder fuses the classification token from both branches through SA, thereby enhancing the model's ability to capture interactions between pixel-level and superpixel-level features. We compared and analyzed seven advanced HSI classification algorithms, and experiments showed that APSFFT outperforms other state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Expansion of Coastal Mangrove Forest in Guangxi Beibu Gulf: Patterns, Drivers and Impacts","authors":"Ziyu Sun, Weiguo Jiang, Ziyan Ling, Jun Sun, Ze Zhang, Shihui Huang, Qiuling Li","doi":"10.1109/jstars.2024.3454976","DOIUrl":"https://doi.org/10.1109/jstars.2024.3454976","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multisensor Glacier Surface Classification Using Confidence-Aware Explainable Inverse-Mapping Neural Network","authors":"Gunjan Joshi;Celia A. Baumhoer;Andreas J. Dietz;Ryo Natsuaki;Akira Hirose","doi":"10.1109/JSTARS.2024.3454789","DOIUrl":"10.1109/JSTARS.2024.3454789","url":null,"abstract":"Mapping snow cover at the end of the ablation season allows us to extract the snow line altitude (SLA). The SLA is an important proxy for the equilibrium line altitude of a glacier and an indicator of glacier health. With the increase in both active and passive remote sensing satellites, the accuracy and effectiveness of glacier monitoring can be enhanced, as the two sensors offer complementary information. In this article, we focus on the combination of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to perform glacial classification using an explainable neural network and thereafter determine SLA. In addition, confidence-aware inverse mapping dynamics is used to understand the result reliability and the individual sensor contributions. The proposed method is applied to the Great Aletsch Glacier in the European Alps, where an overall accuracy of 83% is observed compared to the ground truth data. We observe the glacier from 2015 to 2023, noting a retreat of the SLA to higher elevations by 36 to 133 m depending on the region. Apart from climate-related mass loss, the European Alps are also affected by dust deposited during Sahara dust events and contamination from algae. Thus, in this work, we assess the annual presence of contaminated snow on the glacier. The inverse mapping dynamics reveals the contributions of both SAR and optical sensor data in the classification. This multisensor approach is shown to mitigate the limitations of single-source data, providing a comprehensive understanding of glacier dynamics in the context of climate change.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666818","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multisensors Fusion SLAM-Aided Forest Plot Mapping With Backpack Dual-LiDAR System","authors":"Shuhang Yang;Yanqiu Xing;Tao Xing;Hangyu Deng;Zhilong Xi","doi":"10.1109/JSTARS.2024.3451175","DOIUrl":"10.1109/JSTARS.2024.3451175","url":null,"abstract":"The extraction of forest vertical structural parameters plays a crucial role in forest inventory. In recent years, light detection and ranging (LiDAR) has been widely applied in forest inventories due to its powerful 3-D reconstruction capabilities. The backpack laser scanning (BLS) is a lightweight LiDAR platform that significantly enhances the efficiency and accuracy of forest inventory. To address the issues of Global Navigation Satellite System (GNSS) signal occlusion and LiDAR scanning blind areas under the canopy, a multisensors fusion Simultaneous Localization and Mapping (SLAM) algorithm has been proposed, and a BLS device has been set up. The proposed SLAM algorithm fuses both horizontal and vertical LiDAR data by extracting the planar surface models. In addition, the proposed similar stem features are added to the feature point extraction in forest mapping. The accuracy of the results is validated through standing tree position, diameter at breast height (DBH) and tree height. When compared to other classic SLAM methods, the proposed method achieves 100% accuracy in standing tree extraction, reduces the error in DBH extraction by 85.56% (with an error of 2.05 cm), and decreases the error in tree height extraction by 83.44% (with an error of 0.79 m). The results show that the problem of poor GNSS under the canopy can be effectively addressed by the proposed SLAM algorithm in the study. Furthermore, multisensor data fusion and stem feature addition can provide more complete data support and more robust matching constraints, ultimately resulting in more accurate point cloud mapping.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PTCTV-KMC: Infrared Small Target Detection Using Joint Partial Tensor Correlated Total Variation and K-Means Clustering","authors":"Zixu Huang, Erwei Zhao, Wei Zheng, Yan Wen, Xiaodong Peng, Wenlong Niu, Zhen Yang","doi":"10.1109/jstars.2024.3454150","DOIUrl":"https://doi.org/10.1109/jstars.2024.3454150","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICL-Net: Inverse Cognitive Learning Network for Remote Sensing Image Dehazing","authors":"Weida Dong;Chunyan Wang;Xiping Xu","doi":"10.1109/JSTARS.2024.3454754","DOIUrl":"10.1109/JSTARS.2024.3454754","url":null,"abstract":"When imaging the Earth's surface, space-based optical imaging sensors are inevitably interfered by scattering media, such as clouds and haze, resulting in serious degradation of remote sensing images they capture. To enhance the quality of remote sensing images and mitigate the influence of clouds, haze, and other media, we construct a novel approach called the inverse cognitive learning network. The network mainly consists of multiscale inverse cognitive learning blocks that we designed. It has the capability to extract image features at multiple scales, adaptively focus on the global information and location-related local information, and effectively constrain the haze. In the multiscale inverse cognitive learning block, we embed the designed inverse cognitive learning module and parallel haze constraint module. The inverse cognitive learning module simulates the inverse process of human brain cognitive image, and gradually learns the haze information from the depth, moderate, and breadth channel features. The parallel haze constraint module integrates the extracted haze information through a dual-branch approach to realize strong constraints on haze features. Experimental results indicate that our approach notably enhances the clarity of remote sensing images that suffer from cloud cover and haze, and possesses more perfect haze removal effect and robustness than state-of-the-art dehazing approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10665990","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response Characteristics of FY-3E Outgoing Longwave Radiation to Impending Earthquakes Based on the ATSCTF Algorithm: A Case Study of the 2023 Türkiye Double Earthquakes","authors":"Jing Cui;Siquan Yang;Haizhen Zhang;Jun Liu;Wenliang Jiang;Ji Wei;Lin Wang;Yalan Huang;Chenyu Ma","doi":"10.1109/JSTARS.2024.3454693","DOIUrl":"10.1109/JSTARS.2024.3454693","url":null,"abstract":"The FengYun-3E (FY-3E) satellite was successfully launched on 5 July 2021. It is a polar-orbiting meteorological satellite and the world's first civilian morning and night orbit meteorological satellite. It carries a medium-resolution spectral imager-LL payload that delivers high-resolution outgoing longwave radiation (OLR) products. OLR can reflect more atmospheric change information, is more sensitive to temperature change, and can better reflect the entire surface atmospheric system, which is conducive to the in-depth understanding of the seismic sphere coupling module. OLR is widely used in seismic infrared anomaly extraction. The additive tectonic stress from the celestial tide-generating force (ATSCTF) seismic anomaly extraction algorithm has been continuously improved. However, the threshold of seismic thermal anomaly is mostly based on experience. The results of the quartile algorithm are objective, and it has some advantages in recognizing abnormal values. Whether the FY-3E can be used for seismic risk monitoring remains to be investigated. In this study, the 2023 Türkiye double earthquakes are taken as an example; the seismic anomalies of FY-3E OLR data are extracted based on the ATSCTF algorithm, and the anomaly threshold is determined via the quartile method. The results show that the ATSCTF algorithm based on the quartile threshold method is suitable for FY-3E data, and FY-3E OLR data have a certain response to pre-earthquake radiation anomalies, which can be used for seismic infrared anomaly tracking. The comprehensive use of ascending and descending data can better serve the extraction of seismic anomalies. High-resolution temporal remote sensing data are more conducive to the extraction of seismic anomalies via ATSCTF. The research and practice of seismic risk monitoring based on the ATSCTF algorithm can be further strengthened.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on the Few-Shot SAR Target Recognition","authors":"Junjun Yin;Changxian Duan;Hongbo Wang;Jian Yang","doi":"10.1109/JSTARS.2024.3454266","DOIUrl":"10.1109/JSTARS.2024.3454266","url":null,"abstract":"Synthetic aperture radar (SAR) has the advantage of providing imaging capabilities throughout the day and under all-weather conditions, which makes it particularly important for Earth observation applications. Recently, the utilization of deep learning for SAR image recognition has become a crucial discipline in radar image interpretation since the deepened networks can generate the high-dimensional features and make the function fit accurately when with a large amount of training samples. However, for SAR images, the accurate annotation demands significant effort, expert knowledge, and is prone to errors due to the effect of noise. The lack of SAR-labeled data limits the application of deep neural networks, which usually need a large number of training samples. Consequently, the task of recognizing SAR targets in the scenario with a few training samples has emerged as a significant research interest and, accordingly, the few-shot target recognition technique was introduced and has shown great potential. This article provides a summary of recent advancements in few-shot SAR image target recognition. First, this article outlines the concept of few-shot learning and discusses the dataset specific to the SAR recognition field. Subsequently, it delves into a detailed categorization of methods for recognizing few-shot SAR targets, which include approaches based on the transfer learning, data augmentation, metalearning, and model-based strategies. Finally, it examines both qualitative and quantitative aspects of SAR automatic target recognition technology utilizing few-shot learning, highlights certain challenges and crucial issues that require great attention, and offers a perspective on future research opportunities.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TSPNet: Temporal-Spatial Pyramid Network for Infrared Maritime Object Detection","authors":"Meng Zhang;Lili Dong;Zhichao Huang;Markus Flierl","doi":"10.1109/JSTARS.2024.3452674","DOIUrl":"10.1109/JSTARS.2024.3452674","url":null,"abstract":"Infrared object detection is one of the critical technologies for maritime search and rescue. However, it is still challenging due to the strong background clutter interference and the lack of small object information. We proposed a temporal-spatial pyramid network for infrared maritime object detection. We proposed a nested temporal pyramid to represent the temporal features through motion differences maps and energy accumulation maps to distinguish the wave clutter and objects. We proposed a dense spatial pyramid to learn the spatial features and the differences between temporal maps and then to clarify and locate objects. For training, we designed a scale-related composite loss function with correlated location description and weighted confidence loss. Finally, based on the ablation and comparison experiments, the proposed method performs better on maritime infrared sequences.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Riparian Habitat Conservation: A Spatial Approach Using Aerial and Space Technologies","authors":"Ravindra Nath Tripathi;Aishwarya Ramachandran;Vikas Tripathi;Ruchi Badola;S. A. Hussain","doi":"10.1109/JSTARS.2024.3454453","DOIUrl":"10.1109/JSTARS.2024.3454453","url":null,"abstract":"Riparian habitats are the most crucial yet the most fragile ecosystems, focal to safeguarding both the aquatic and terrestrial regions. Technology such as remote sensing, now powered by cloud-based server-side processing of high-resolution satellite imagery, and Big Data analytics, such as Google Earth Engine (GEE) combined with uncrewed aerial vehicles (UAVs), have accentuated ecological monitoring of natural habitats. This study leverages a nested approach to remote sensing, combining satellite data and UAV imagery to evaluate the present condition of riparian habitats along the Ganga River in the Upper Gangetic Plains. We used GEE to analyze Sentinel data and identify critical habitats, encompassing wetlands, grasslands, scrublands, plantations, river islands, and riparian forests in the study area. Strategic locations covering 291 km\u0000²\u0000 area were delineated, and over 1000 patches of 1 ha were isolated, with the largest patch of 23.99 km\u0000²\u0000 in Haiderpur. Furthermore, UAV-based data were collected for key identified regions. The status of a total of 284 field surveyed points was categorized as 29 intact grassland patches, 87 good habitat patches, 25 patches recently converted to agriculture, and 60 patches being converted to agriculture, remaining plantations, and waterbodies. UAV-based raster thematic maps of four key habitat regions generated using object-based image analysis classification found a promising approach for high-precision riparian habitat mapping, monitoring, and management, offering data quality, cost optimization, and time savings with an overall accuracy of 98% and kappa coefficient 0.97. UAV are, thus, effective tools for reach-level assessment of freshwater habitats, especially of smaller stream networks, retaining fine-scale riverscape information of the mosaic of land use and vegetation types.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}