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

Are Mediators of Grief Reactions Better Predictors Than Risk Factors? A Study Testing the Role of Satisfaction With Rituals, Perceived Social Support, and Coping Strategies. 悲伤反应的调解因素比风险因素更能预测悲伤反应吗？一项测试仪式满意度、感知的社会支持和应对策略作用的研究。

2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-01 Epub Date: 2023-07-30 DOI: 10.1177/10541373231191316

Jacques Cherblanc, Emmanuelle Zech, Susan Cadell, Isabelle Côté, Camille Boever, Manuel Fernández-Alcántara, Christiane Bergeron-Leclerc, Danielle Maltais, Geneviève Gauthier, Chantal Verdon, Josée Grenier, Chantale Simard

引用次数: 0

PS-GAN: Pseudo Supervised Generative Adversarial Network With Single Scale Retinex Embedding for Infrared and Visible Image Fusion PS-GAN：用于红外和可见光图像融合的单尺度视网膜嵌入伪监督生成对抗网络

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-09 DOI: 10.1109/JSTARS.2024.3509684

Jin Qi;Deboch Eyob Abera;Jian Cheng

引用次数: 0

DLAFNet: Direct LiDAR-Aerial Fusion Network for Semantic Segmentation of 2-D Aerial Image and 3-D LiDAR Point Cloud

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-05 DOI: 10.1109/JSTARS.2024.3511517

Wei Liu;He Wang;Yicheng Qiao;Haopeng Zhang;Junli Yang

{"title":"DLAFNet: Direct LiDAR-Aerial Fusion Network for Semantic Segmentation of 2-D Aerial Image and 3-D LiDAR Point Cloud","authors":"Wei Liu;He Wang;Yicheng Qiao;Haopeng Zhang;Junli Yang","doi":"10.1109/JSTARS.2024.3511517","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3511517","url":null,"abstract":"High-resolution remote sensing image segmentation has advanced significantly with 2-D convolutional neural networks and transformer-based models like SegFormer and Swin Transformer. Concurrently, the rapid development of 3-D convolution techniques has driven advancements in methods like PointNet and Kernel Point Convolution for 3-D LiDAR point cloud segmentation. Traditional fusion of aerial imagery and LiDAR data often relies on digital surface models or other features extracted from LiDAR point clouds, incorporating them as depth channels into image data. In this article, we propose a novel approach called Direct LiDAR-Aerial Fusion Network, which directly integrates multispectral images (RGB) and LiDAR point cloud data for semantic segmentation. Experiments on the modified GRSS18 dataset demonstrate that our method achieves an overall accuracy (OA) of 79.88%, outperforming conventional approaches. By fusing RGB and LiDAR features, our technique improves OA by 1.77% and mean Intersection over Union by 0.83%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1864-1875"},"PeriodicalIF":4.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844393","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}

引用次数: 0

Dual-Polarimetric SAR Measurements to Observe Liquefaction Surface Manifestations

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-04 DOI: 10.1109/JSTARS.2024.3509645

Ferdinando Nunziata;Anna Verlanti;Nicola Angelo Famiglietti;Maurizio Migliaccio;Annamaria Vicari

{"title":"Dual-Polarimetric SAR Measurements to Observe Liquefaction Surface Manifestations","authors":"Ferdinando Nunziata;Anna Verlanti;Nicola Angelo Famiglietti;Maurizio Migliaccio;Annamaria Vicari","doi":"10.1109/JSTARS.2024.3509645","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3509645","url":null,"abstract":"In this study, a methodology is proposed to use dual-polarimetric synthetic aperture radar (SAR) to identify the spatial distribution of soil liquefaction. The latter is a phenomenon that occurs in conjunction with seismic events of a magnitude generally higher than 5.5–6.0 and which affects loose sandy soils located below the water table level. The methodology consists of two steps: first the spatial distributions of soil liquefaction is estimated using a constant false alarm rate method applied to the SPAN metric, namely the total power associated with the measured polarimetric channels, which is ingested into a bitemporal approach to sort out dark areas not genuine. Second, the obtained masks are read in terms of the physical scattering mechanisms using a child parameter stemming from the eigendecomposition of the covariance matrix—namely the degree of polarization. The latter is evaluated using the coseismic scenes and contrasted with the preseismic one to have rough information on the time-variability of the scattering mechanisms occurred in the area affected by soil liquefaction. Finally, the obtained maps are qualitatively contrasted against state-of-the-art optical and interferometric SAR methodologies. Experimental results, obtained processing a time-series of ascending and descending Sentinel-1 SAR scenes acquired during the 2023 Türkiye–Syria earthquake, confirm the soundness of the proposed approach.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1792-1801"},"PeriodicalIF":4.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844391","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}

引用次数: 0

Deep-Learning-Based Semantic Change Detection for Urban Greenery and Comprehensive Urban Areas

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-04 DOI: 10.1109/JSTARS.2024.3511597

Aisha Javed;Taeheon Kim;Changhui Lee;Youkyung Han

{"title":"Deep-Learning-Based Semantic Change Detection for Urban Greenery and Comprehensive Urban Areas","authors":"Aisha Javed;Taeheon Kim;Changhui Lee;Youkyung Han","doi":"10.1109/JSTARS.2024.3511597","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3511597","url":null,"abstract":"Urban greenery is important for maintaining ecological balance and enhancing urban ecosystems. However, it is significantly degrading due to human activities and natural disasters, making it essential to monitor both urban greenery and the overall urban environment. Recent advancements in remote sensing and deep learning technologies have led to the development of semantic change detection (SCD) techniques, which offer more detailed analysis than binary change detection. Detecting changes in natural greenery within urban environments using general SCD techniques is challenging due to the similar spectral characteristics of natural and artificial greenery. Therefore, this study proposes a direct SCD approach focusing on urban green spaces and nongreenery-related changes. This approach distinguishes between new and degraded greenery regions and categorizes them into distinct classes alongside nongreenery changes. Key innovations include the integration of atrous spatial pyramid pooling and an updated spatial attention module, enhancing the network's ability to capture objects of varying shapes and sizes within urban settings. The methodology was evaluated using two open-source datasets, SEmantic Change detectiON Dataset (SECOND) and Wuhan urban sematic understanding (WUSU), customized to emphasize urban greenery changes. Results demonstrate that our approach significantly outperforms the existing SCD techniques in accurately detecting and categorizing new and degraded greenery regions alongside overall urban changes. The proposed method achieved superior performance in terms of separated kappa, reaching 17.72% on the SECOND dataset and 36.18% on the WUSU dataset. This study addresses the limitations of current methods in monitoring urban greenery, providing an efficient tool for assessing the impact of urbanization and natural disasters on urban greenery and the broader urban environment.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1841-1852"},"PeriodicalIF":4.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844383","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}

引用次数: 0

Multichannel Enhanced Millimeter-Wave SAR Imaging via Low-Rank Tensor-Train Decomposition

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-03 DOI: 10.1109/JSTARS.2024.3509477

Bangjie Zhang;Gang Xu;Xiang-Gen Xia;Jianlai Chen;Rui Zhou;Shuai Shao;Wei Hong

{"title":"Multichannel Enhanced Millimeter-Wave SAR Imaging via Low-Rank Tensor-Train Decomposition","authors":"Bangjie Zhang;Gang Xu;Xiang-Gen Xia;Jianlai Chen;Rui Zhou;Shuai Shao;Wei Hong","doi":"10.1109/JSTARS.2024.3509477","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3509477","url":null,"abstract":"Millimeter-wave (mmWave) synthetic aperture radar (SAR) has found wide applications in autonomous driving, landslide detection, urban mapping, etc. However, the high propagation loss of mmWave bands and the limitation of transmitting power have led to limited imaging performance for mmWave SAR. In this article, an enhanced SAR imaging framework that combines along-track multiple channels is proposed using a low-rank tensor-train (TT) decomposition method, which is applicable for a co-located multiple input multiple output (MIMO) array or a phased-transmitting-digital-receiving array. First, the multichannel images are stacked into a tensor form after SAR imaging on individual channels and spatial variant array phase correction for each pixel. Then, the low-rank property of tensor stack is exploited and the TT model is utilized to find the redundancy and leverage the intrinsic structure of image stack. In addition, ket augmentation is introduced to exhibit the local data structure more clearly than the original tensor under TT decomposition. Finally, tensor-train nuclear norm is used to relax the NP-hard problem with low-rank constraint and the minimization problem is solved in the framework of alternating direction method of multipliers for enhanced imaging. The proposed algorithm can effectively improve the working distance and image quality of mmWave SAR. Numerical experiments using simulated data of MIMO SAR and measured data collected by a ground-based phased-transmitting-digital-receiving array system are carried out to verify the performance of the proposed algorithm.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1551-1561"},"PeriodicalIF":4.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10776754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844568","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}

引用次数: 0

Variable-Stepsize Multilayer Neural Network for Subpixel Target Detection in Hyperspectral Imaging

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-03 DOI: 10.1109/JSTARS.2024.3508261

Edisanter Lo

{"title":"Variable-Stepsize Multilayer Neural Network for Subpixel Target Detection in Hyperspectral Imaging","authors":"Edisanter Lo","doi":"10.1109/JSTARS.2024.3508261","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3508261","url":null,"abstract":"Conventional algorithms for subpixel target detection of a rare target in hyperspectral imaging are derived from the generalized likelihood ratio test. Artificial neural networks are designed for classification, which needs large samples of pixels for training background and target classes, but have a problem with subpixel target detection, which typically has only one target pixel for training the target class. The current detection algorithm for subpixel target detection based on neural networks uses a single-layer neural network and stochastic gradient descent method with variable stepsize to solve the optimization problem. The objective of this paper is to improve the current algorithm by developing a detection algorithm for subpixel target detection based on a multilayer neural network and stochastic gradient descent method with variable stepsize. The decision boundary is linear for the single-layer neural network and nonlinear for the multi-layer neural network. Experimental results from two hyperspectral images, one with simulated subpixel target pixels for validating the algorithm and the other with actual subpixel target pixels for validation, have shown that the proposed algorithm can perform better than the conventional algorithms.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1718-1733"},"PeriodicalIF":4.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10774156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844472","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}

引用次数: 0

STA-AgriNet: A Spatio-Temporal Attention Framework for Crop Type Mapping from Fused Multi-Sensor Multi-Temporal SITS

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-02 DOI: 10.1109/JSTARS.2024.3510468

Jayakrishnan Anandakrishnan;Venkatesan Meenkaski Sundaram;Prabhavathy Paneer

{"title":"STA-AgriNet: A Spatio-Temporal Attention Framework for Crop Type Mapping from Fused Multi-Sensor Multi-Temporal SITS","authors":"Jayakrishnan Anandakrishnan;Venkatesan Meenkaski Sundaram;Prabhavathy Paneer","doi":"10.1109/JSTARS.2024.3510468","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3510468","url":null,"abstract":"Precise and timely crop type mapping delivers insights into crop growth statistics and ensures food security for growing economies. Automated mapping is crucial in several agricultural applications, including crop wear assessment and yield forecasting. The high-resolution multispectral optical data can deliver essential spatial-spectral characteristics; however, these are typically impeded by unfavorable weather and obstructions, resulting in poor classification. Recent advancements in multisensor data fusion techniques have focused on integrating optical data with auxiliary synthetic aperture radar (SAR) data to mitigate misclassification issues. However, current optical-SAR fusion techniques have yet to effectively address the incorporation of spatial-spectral characteristics with long-term temporal dependencies of satellite image time series (SITS). This article proposes an optical-SAR deep fusion framework, STA-AgriNet, that integrates a U-Net encoder–decoder with spatial-temporal attention frameworks to enable superior extraction of long-term spatial-temporal dependencies for reliable crop-type mapping. The spectral spatial feature mapper (SSFM), mixed parallel spatial attention (MPSA), and spatio-temporal attention mapper (STAM) modules of the STA-AgriNet extract key classification-defining, discriminative patterns for semantic segmentation. The STA-AgriNet framework is evaluated against current state-of-the-art (SOTA) methods and demonstrates superior performance across multiple metrics, achieving an accuracy of 83.61% with a minimal inference time of 10.09 s and a compact parameter count of 0.97 million. The model also excels in other key evaluation metrics, establishing its overall effectiveness and efficiency compared to existing techniques.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1817-1826"},"PeriodicalIF":4.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772604","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844473","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}

引用次数: 0

CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-12-02 DOI: 10.1109/JSTARS.2024.3510545

Zixu Li;Jintao Song;Genji Yuan;Jinjiang Li

{"title":"CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening","authors":"Zixu Li;Jintao Song;Genji Yuan;Jinjiang Li","doi":"10.1109/JSTARS.2024.3510545","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3510545","url":null,"abstract":"Restricted by the development of contemporary sensors, we can only acquire multispectral images (MS) and high-resolution panchromatic (PAN) images separately. The purpose of pansharpening methods is to combine the rich spectral-spatial information contained in MS and PAN images to generate the high-resolution multispectral image. Most existing pansharpening methods either separately extract feature information from MS and PAN images or extract feature information after concatenating MS and PAN images, lacking the utilization of complementary information throughout the feature extraction process. Motivated by the advancements in optimization algorithm and the state space model, we introduce a convolutional dictionary learning with state space model for pansharpeningin this article. Our network comprises two parts: the encoder and the decoder. In the encoder part, we begin by building an observation model to capture the common and unique information between MS and PAN images. Subsequently, we continuously iterate and optimize the network parameters using the approximate gradient algorithm. Meanwhile, we utilize the powerful long-range dependence modeling capability of the SSM to comprehensively extract feature information from the images. In the decoder part, we propose both a detail enhancement block and an adaptive weight learning block to strengthen the model's ability to extract detailed feature information from the images. To demonstrate the superiority of our proposed method, we conduct comparative experiments with current state-of-the-art pansharpening methods on three benchmark datasets: QB, GF2, and WV3. Experimental results prove that our method exhibits the best performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1734-1751"},"PeriodicalIF":4.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844471","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}

引用次数: 0

Early Invasion Process Monitoring of Spartina Alterniflora Using Long Time Series High-Resolution Satellite Remote Sensing Imagery

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-29 DOI: 10.1109/JSTARS.2024.3506985

Guangbo Ren;Yabin Hu;Feng Lu;Yingfeng Zhou;Jianbu Wang;Peiqiang Wu;Yi Ma

{"title":"Early Invasion Process Monitoring of Spartina Alterniflora Using Long Time Series High-Resolution Satellite Remote Sensing Imagery","authors":"Guangbo Ren;Yabin Hu;Feng Lu;Yingfeng Zhou;Jianbu Wang;Peiqiang Wu;Yi Ma","doi":"10.1109/JSTARS.2024.3506985","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3506985","url":null,"abstract":"<italic>Spartina alterniflora (S. alterniflora)\u0000 rapidly invade new habitats by seed jumping propagation, and the early invasion process is characterized by discrete large patches and diffuse distribution small-scale patches. Monitoring these early-invasion-stage patches is very important for effectively stopping the \u0000<italic>S. alterniflora\u0000 invasion trends. In this study, a long time series of high spatial resolution (0.5 and 1 m) satellite remote sensing images from 2010 to 2020 were used to monitor the early invasion period of \u0000<italic>S. alterniflora\u0000. Using remote sensing technology, the early invasion behavior of \u0000<italic>S. alterniflora\u0000 at patch scale was monitored and analyzed in the Yellow River Delta with the object-oriented image segmentation and support vector machine classification methods. The results indicate that the discrete patches were incorporated into bigger patches or gathering regions within a few years: 56.13% of the independent large patches lasted only for 1 year, and only 0.25% of which can last for 10 years. Second, we newly found out that the newborn \u0000<italic>S. alterniflora\u0000 seedlings are uniformly distributed between discrete large patches and then quickly aggregate into a large gathering area within the next 2 years. Third, for the area that was undergoing invasion, the mean \u0000<italic>S. alterniflora\u0000 coverage rate was stable at approximately 50%. Fourth, dominant factors that determined the invasion trend of \u0000<italic>S. alterniflora\u0000 are the elevation of the intertidal zone, the tidal channel distribution and the topographic changes caused by \u0000<italic>S. alterniflora\u0000 invasion.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"1317-1328"},"PeriodicalIF":4.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10771951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810657","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}

引用次数: 0