IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献_第5页

Infrared Moving Small Target Detection Based on Spatial–Temporal Feature Fusion Tensor Model 基于时空特征融合张量模型的红外移动小目标检测

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/JSTARS.2024.3491221

Deyong Lu;Wei An;Haibo Wang;Qiang Ling;Dong Cao;Miao Li;Zaiping Lin

{"title":"Infrared Moving Small Target Detection Based on Spatial–Temporal Feature Fusion Tensor Model","authors":"Deyong Lu;Wei An;Haibo Wang;Qiang Ling;Dong Cao;Miao Li;Zaiping Lin","doi":"10.1109/JSTARS.2024.3491221","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3491221","url":null,"abstract":"Infrared moving small target detection is an important and challenging task in infrared search and track system, especially in the case of low signal-to-clutter ratio (SCR) and complex scenes. The spatial–temporal information has not been fully utilized, and there is a serious imbalance in their exploitation, especially the lack of long-term temporal characteristics. In this article, a novel method based on the spatial–temporal feature fusion tensor model is proposed to solve these problems. By directly stacking raw infrared images, the sequence can be transformed into a third-order tensor, where the spatial–temporal features are not reduced or destroyed. Its horizontal and lateral slices can be viewed as 2-D images, showing the change of gray values of horizontal/vertical fixed spatial pixels over time. Then, a new tensor composed of several serial slices are decomposed into low-rank background components and sparse target components, which can make full use of the temporal similarity and spatial correlation of background. The partial tubal nuclear norm is introduced to constrain the low-rank background, and the tensor robust principal component analysis problem is solved quickly by the alternating direction method of multipliers. By superimposing all the decomposed sparse components into the target tensor, small target can be segmented from the reconstructed target image. Experimental results of synthetic and real data demonstrate that the proposed method is superior to other state-of-the-art methods in visual and numerical results for targets with different sizes, velocities, and SCR values under different complex backgrounds.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"78-99"},"PeriodicalIF":4.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713828","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

Evaluation of Total Precipitable Water Trends From Reprocessed MiRS SNPP ATMS Observations, 2012–2021 对 2012-2021 年经重新处理的全球降水监测系统（MiRS）SNPP ATMS 观测数据得出的可降水总量趋势进行评估

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-01 DOI: 10.1109/JSTARS.2024.3481444

Yan Zhou;Christopher Grassotti;Quanhua Liu;Shuyan Liu;Yong-Keun Lee

{"title":"Evaluation of Total Precipitable Water Trends From Reprocessed MiRS SNPP ATMS Observations, 2012–2021","authors":"Yan Zhou;Christopher Grassotti;Quanhua Liu;Shuyan Liu;Yong-Keun Lee","doi":"10.1109/JSTARS.2024.3481444","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3481444","url":null,"abstract":"Total precipitable water (TPW) is defined as the vertically integrated column water vapor from the earth's surface to the top of the atmosphere. TPW is a key element of the hydrological cycle and is responsive to changes in global climate related to greenhouse-gas-induced warming. In this research, we focus on trend analysis using the TPW retrieval product from the recently reprocessed Microwave Integrated Retrieval System (MiRS) Suomi National Polar-Orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS) data and compare it with ERA5 reanalysis. The primary results show that the global TPW trend during 2012–2021 from reprocessed SNPP ATMS is 0.46 mm/decade, in relatively good agreement with the trend from ERA5 of 0.39 mm/decade. Trends for tropical and mid-latitude subregions are also in good agreement, with essentially the same trend of 0.43 mm/decade seen in both datasets in the mid-latitudes. Both the datasets show a large positive anomaly associated with the strong El Nino event in 2015–2016, which increased TPW amounts in the tropics. We also found that the TPW trend is not uniformly distributed spatially, with significant regional variations in both sign and amplitude. Nevertheless, the spatial patterns from MiRS SNPP ATMS retrievals and ERA5 analyses are in very good agreement. Both the datasets show that positive TPW trends in terms of relative percentage in the polar regions were on par with those seen in lower latitudes. The results suggest that water vapor observations from a single polar-orbiting microwave instrument with only two local observation times daily may be sufficient to characterize trends in TPW.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"19798-19804"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636568","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

Multiscale Attention-UNet-Based Near-Real-Time Precipitation Estimation From FY-4A/AGRI and Doppler Radar Observations 基于 FY-4A/AGRI 和多普勒雷达观测数据的多尺度注意力网络近实时降水估算

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-01 DOI: 10.1109/JSTARS.2024.3488854

Dongling Wang;Shanmin Yang;Xiaojie Li;Jing Peng;Hongjiang Ma;Xi Wu

{"title":"Multiscale Attention-UNet-Based Near-Real-Time Precipitation Estimation From FY-4A/AGRI and Doppler Radar Observations","authors":"Dongling Wang;Shanmin Yang;Xiaojie Li;Jing Peng;Hongjiang Ma;Xi Wu","doi":"10.1109/JSTARS.2024.3488854","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3488854","url":null,"abstract":"Extreme precipitation events greatly threaten people's daily lives and safety, making accurate and timely precipitation estimation especially critical. However, common methods like radar and satellite remote sensing have limitations due to coverage and environmental factors. Existing deep learning models struggle with complex scenarios and multisource data correlations. These make the precipitation estimation tasks challenging. This article proposes a Multiscale Dual Cross-Attention UNet (MS-DCA-UNet) model for near-real-time precipitation estimation. It integrates Doppler weather radar and FY-4A satellite data to overcome single-source data limitations. To narrow the semantic gap among the encoder feature maps, the MS-DCA-UNet model introduces a dual-cross attention (DCA) module at the skip connections of the backbone network U-Net. The DCA module mainly employs a channel cross-attention and a spatial cross-attention to capture remote dependencies and enable multiscale feature fusion. A multiscale convolution module is designed to reduce the risk of the model falling into local optima. It is a multibranch upsampling strategy that runs parallel to the decoder. Experimental results show that the Critical Success Index (CSI), Root Mean Square Error (RMSE), and Pearson's Correlation Coefficient (CC) of MS-DCA-UNet are 0.6033, 0.5949 mm/h, and 0.8460, respectively, with the hourly CMPAS precipitation as the benchmark. These outperform the other comparisons, such as FY-4A QPE, GPM IMERG, U-Net, Attention-UNet, and DCA-UNet on the CSI, RMSE, and CC metrics. MS-DCA-UNet reduces the RMSE of Attention-UNet, UNet, and DCA-UNet by a margin of 34.68% (0.5949 mm/h versus 0.9107 mm/h), 10.24% (0.5949 mm/h versus 0.6628 mm/h), 6.96% (0.5949 mm/h versus 0.6394 mm/h), respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"19998-20011"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672121","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

Evaluation and Modeling of Image Sharpness of Chinese Gaofen-1/2/6/7 Optical Remote-Sensing Satellites Over Time 中国高分一号/二号/六号/七号光学遥感卫星图像清晰度随时间变化的评估与建模

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-01 DOI: 10.1109/JSTARS.2024.3490738

Jiayang Cao;Litao Li;Yonghua Jiang;Xin Shen;Deren Li;Meilin Tan

{"title":"Evaluation and Modeling of Image Sharpness of Chinese Gaofen-1/2/6/7 Optical Remote-Sensing Satellites Over Time","authors":"Jiayang Cao;Litao Li;Yonghua Jiang;Xin Shen;Deren Li;Meilin Tan","doi":"10.1109/JSTARS.2024.3490738","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3490738","url":null,"abstract":"Image sharpness assesses detail visibility in remote-sensing images and measures sensors' details resolution capability. Sensor aging and environmental changes can degrade image sharpness and quality. The Gaofen (GF) satellites provide diverse remote-sensing imagery, but evaluations of their sharpness are limited. In this study, for the GF1/2/6/7 optical remote-sensing satellites in the space-based system of the China High-Resolution Earth Observation System (CHEOS) major special project, we evaluated the relative edge response (RER), full width at half maximum (FWHM), and modulation transfer function (MTF) of the images, using nearly ten years of ground target image data. This measures image sharpness and models how it changes over time with different sensors. Within ten years of on-orbit operation, the RER and MTF (@Nyquist frequency) of GF1/2 are 0.51 and 0.50, and 0.15 and 0.11, respectively. This indicated good image edge and high-frequency detail responsiveness, with FWHM of 1.16 and 1.17, respectively, showing a slight image sharpening. For GF6, the RER, MTF (@Nyquist frequency), and FWHM were 0.42, 0.09, and 1.39, indicating improved sharpening compared with GF1/2 but decreased edge and high-frequency detail response. The RER, MTF (@Nyquist frequency), and FWHM of the panchromatic images of GF7 were 0.32, 0.04, and 1.91, which indicate image blur. Meanwhile, the corresponding indicators for the multispectral images were 0.45, 0.14, and 1.40, better than the panchromatic images. Long-term data showed periodic sharpness variations in satellite images, with GF6s stability and minimal track differences being superior. The dynamic change pattern corresponds to a fourth-order polynomial model.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20150-20163"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713776","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

Impact Assessment of Flood Events Based on Multisource Satellite Remote Sensing: The Case of Kahovka Dam 基于多源卫星遥感的洪水事件影响评估：卡霍夫卡大坝案例

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-01 DOI: 10.1109/JSTARS.2024.3490756

Chen Zuo;Haowei Zhang;Xin Ma;Wei Gong

{"title":"Impact Assessment of Flood Events Based on Multisource Satellite Remote Sensing: The Case of Kahovka Dam","authors":"Chen Zuo;Haowei Zhang;Xin Ma;Wei Gong","doi":"10.1109/JSTARS.2024.3490756","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3490756","url":null,"abstract":"On June 6, 2023, the Kakhovka Dam in Ukraine was damaged, causing a flood that could have had a significant impact on society. A single satellite may not provide sufficient data for timely disaster response. To overcome this, a combination of optical images, a new generation of hydrological monitoring satellite data, and nighttime light data were employed to analyze the disaster. Sentinel-3 provides useful hydrological information for quickly identifying and locating disaster areas, while surface water and ocean topography are able to detect changes in water surface elevation, providing a direct view of the flood's impact on the water area and surface elevation. The datasets both provide information about the extent of the flood area, which is more detailed than that provided by a single source. Furthermore, the NPP-VIIRS data not only reflects the indirect impact of the disaster on the lives and production of the local population, but also provides an intuitive assessment of the damage to the Zaporizhzhya nuclear power plant's power supply. The data showed that the disaster affected an area of about 8 km on either side of the river downstream of the dam. This enables the prediction of the damage and the postdisaster reconstruction strategy from a humanistic perspective. The combination of these three data types enables the specific impact of the disaster to be gauged in terms of its scope, extent, impact on human life, and the postdisaster recovery situation. This provides a scientific reference for the timely formulation and implementation of disaster relief and postdisaster reconstruction measures.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20164-20176"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713777","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

Domain Adaptation for Multilabel Remote Sensing Image Annotation With Contrastive Pseudo-Label Generation 利用对比伪标签生成技术为多标签遥感图像注释进行领域调整

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-01 DOI: 10.1109/JSTARS.2024.3490596

Rui Huang;Mingyang Ma;Wei Huang

{"title":"Domain Adaptation for Multilabel Remote Sensing Image Annotation With Contrastive Pseudo-Label Generation","authors":"Rui Huang;Mingyang Ma;Wei Huang","doi":"10.1109/JSTARS.2024.3490596","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3490596","url":null,"abstract":"Deep-learning-based multilabel remote sensing image annotation (MLRSIA) is receiving increasing attention in recent years. MLRSIA needs a large volume of labeled samples for effective training of the deep models. However, the scarcity of labeled samples is a common challenge in this field. Domain adaptation (DA), aiming to transfer knowledge from label-rich datasets (source domains) to label-scarce datasets (target domains), has become an effective means to address this problem of limited labeled samples. But most of the existing DA models are primarily designed for single-label annotation tasks, leaving the application of DA to multilabel annotation tasks as an open issue. In this article, a DA method for MLRSIA, named contrastive pseudo-label generation (CPLG), is proposed. CPLG mainly consists of two parts: generating and selecting pseudo-labels for the samples in the target domain, and enhancing the cross-domain feature consistency through contrastive learning. Specifically, the soft predictions (or posterior probabilities) and the corresponding pseudo-labels of the target samples are first generated using neighborhood aggregation. Then, a positive and negative pseudo-label selection strategy is designed to refine these pseudo-label. Finally, a contrastive loss is introduced to align the similar sample features between the source and target domains to avoid the pseudo-labels of the target samples being overly biased toward the source domain, further improving the precision of these pseudo-labels. The MLRSIA experiments, conducted across four different DA scenarios on three benchmark datasets, demonstrate the advantages of the proposed CPLG compared to other state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20344-20354"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736498","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

MMAPP: Multibranch and Multiscale Adaptive Progressive Pyramid Network for Multispectral Image Pansharpening MMAPP：用于多光谱图像平锐化的多分支多尺度自适应渐进金字塔网络

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-11-01 DOI: 10.1109/JSTARS.2024.3490755

Zhiqi Zhang;Chuang Liu;Lu Wei;Shao Xiang

{"title":"MMAPP: Multibranch and Multiscale Adaptive Progressive Pyramid Network for Multispectral Image Pansharpening","authors":"Zhiqi Zhang;Chuang Liu;Lu Wei;Shao Xiang","doi":"10.1109/JSTARS.2024.3490755","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3490755","url":null,"abstract":"Pansharpening is the process of integrating two heterogeneous remote sensing images to obtain high-resolution multispectral images, which is crucial for downstream tasks. Existing methods utilizing advanced deep-learning techniques are able to achieve good sharpening results. However, the heterogeneity between diverse source images is not sufficiently considered, which in turn results in distortions in the sharpening results. Addressing this gap, we have developed a multibranch pyramid structure, which can build bridges between diverse source images at various scales. It contains three distinct branches, including the PAN branch, the MS branch, and the fusion branch, which efficiently and seamlessly integrates the data flow in distinct branches by means of the pyramid structure. Furthermore, in order to retain more advantageous information, we have developed a specialized adaptive extraction and integration module (AEIM) for each branch, namely, the texture shrinkage adaptive module for the PAN branch, the spectral information consistency module for the MS branch, and the adaptive fusion module for the fusion branch. These AEIMs are specifically designed to cater to diverse sources and distinct stages of the pansharpening process. The adaptive weights they generate can be used to extract and fuse more advantageous information. Ultimately, high-fidelity sharpening outcomes are obtained by minimizing the reconstruction errors at various scales in distinct branches. Extensive experiments show that our methodology surpasses that of representative advanced methods, while maintaining a high level of efficiency. All implementations will be published at MMAPP.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20129-20149"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713774","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

Assessing Land Degradation and Restoration in Eastern China Grasslands from 1985 to 2018 Using Multitemporal Landsat Data 利用多时相大地遥感数据评估 1985 年至 2018 年中国东部草原的土地退化和恢复情况

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-31 DOI: 10.1109/JSTARS.2024.3483992

Caixia Liu;Huabing Huang;John M. Melack;Ye Tian;Jinxiong Jiang;Xiao Fu;Zhiguo Cao;Shaohua Wang

{"title":"Assessing Land Degradation and Restoration in Eastern China Grasslands from 1985 to 2018 Using Multitemporal Landsat Data","authors":"Caixia Liu;Huabing Huang;John M. Melack;Ye Tian;Jinxiong Jiang;Xiao Fu;Zhiguo Cao;Shaohua Wang","doi":"10.1109/JSTARS.2024.3483992","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3483992","url":null,"abstract":"The grassland ecosystems of Xilingol, China, characteristically part of the vast Eurasian steppe, are currently facing two challenges: natural variations and anthropogenic stress, which are leading to significant degradation. This article harnesses a sequence of high-resolution (30 m) land cover and greenness trend maps derived from multiyear Landsat imagery to describe these ecologically critical shifts over a landscape spanning more than 200 000 km\u00002\u0000. By leveraging random forest models complemented with phenological patterns, we streamlined the generation of land cover maps, securing overall accuracies upwards of 94% across eight categorical classifications, as substantiated by rigorous validation. Between 1985 and 2000, there were significant changes in the landscape, such as an increase in farmland of about 4.0 × 10\u00003\u0000 km\u00002\u0000, mostly at the expense of natural grasslands and wetlands. Throughout the study period, an ongoing trend is the noticeable shrinkage of water bodies with the biggest reduction of wetlands reported between 1995 and 2015. Open-pit mining regions began to increase with the start of the 21st century, and from 1985 to the present, urbanization drove the growth of impervious surfaces. These maps offer powerful visual representations of major land use changes, capturing the expansion of surface mining, the retreat of wetland areas, and the growth of urban areas. Therefore, our findings compose an essential part in the documentation and comprehension of the details of wetland reduction, cropland intensification, surface water decline, and rapid urban growth, providing crucial information to conservationists and policymakers working toward sustainable ecosystem management.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"19328-19342"},"PeriodicalIF":4.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579179","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

Generation and Assessment of Digital Elevation Models by Combining Sentinel-1A and Sentinel-1B Data in Mountain Glacier Area 结合高山冰川地区哨兵-1A 和哨兵-1B 数据生成和评估数字高程模型

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-31 DOI: 10.1109/JSTARS.2024.3489576

Lili Yan;Hongxing Li;Xiaohua Hao;Jian Wang;Zhenliang Yin;Junyan Liu

{"title":"Generation and Assessment of Digital Elevation Models by Combining Sentinel-1A and Sentinel-1B Data in Mountain Glacier Area","authors":"Lili Yan;Hongxing Li;Xiaohua Hao;Jian Wang;Zhenliang Yin;Junyan Liu","doi":"10.1109/JSTARS.2024.3489576","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3489576","url":null,"abstract":"The launch of Sentinel-1 satellite introduces a novel approach for synthetic aperture radar (SAR) interferometry (InSAR). However, its capabilities for topographic mapping are reportedly limited. It is very challenging to create high-quality digital elevation models (DEMs) from InSAR data in mountain area. The main goal of the study was to generate a new high-quality DEM by combining Sentinel-1A and Sentinel-1B data with a short temporal baseline and multiple perpendicular baselines from 43 to 143 m. Five DEMs were produced from five interferometric pairs. The five DEMs were fused to generate a more reliable fused DEM. The performance of the new DEMs was evaluated against ICESat-2/ATL06 product and global DEM products (NASADEM, advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM), and advanced land observing satellite ALOS World 3D-30 m). The results showed that all Sentinel-1 DEMs performed better than ASTER GDEM, four of DEMs had higher accuracies than NASADEM. The fused DEM with the vertical error of 9.08 m for steep terrain (slope>20°) revealed higher accuracy than three global DEMs. The accuracy of DEM was related to terrain slope and land cover type. The accuracies of DEMs decreased as slope increased. The DEMs in glacier area revealed higher errors than those in bare rock. Besides, there was no clear relationship between the perpendicular baseline and vertical accuracy of DEM. The interferometric pair with the shortest baseline (43 m) produced the worst quality DEM, while the interferometric pair with slightly shorter baseline (69 m) produced the highest quality DEM. The study revealed the outstanding accuracy of the new DEM product, which is very valuable data for local glacier research.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"642-654"},"PeriodicalIF":4.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736512","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

Popeye: A Unified Visual-Language Model for Multisource Ship Detection From Remote Sensing Imagery 大力水手从遥感图像中进行多源船舶探测的统一视觉语言模型

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2024-10-30 DOI: 10.1109/JSTARS.2024.3488034

Wei Zhang;Miaoxin Cai;Tong Zhang;Guoqiang Lei;Yin Zhuang;Xuerui Mao

{"title":"Popeye: A Unified Visual-Language Model for Multisource Ship Detection From Remote Sensing Imagery","authors":"Wei Zhang;Miaoxin Cai;Tong Zhang;Guoqiang Lei;Yin Zhuang;Xuerui Mao","doi":"10.1109/JSTARS.2024.3488034","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3488034","url":null,"abstract":"Ship detection needs to identify ship locations from remote sensing scenes. Due to different imaging payloads, various appearances of ships, and complicated background interference from the bird's eye view, it is difficult to setup a unified paradigm for achieving multisource ship detection. To address this challenge, in this article, leveraging the large language models powerful generalization ability, a unified visual-language model called Popeye is proposed for multisource ship detection from RS imagery. Specifically, to bridge the interpretation gap across the multisource images for ship detection, a novel unified labeling paradigm is designed to integrate different visual modalities and the various ship detection ways, i.e., horizontal bounding box and oriented bounding box. Subsequently, the hybrid experts encoder is designed to refine multiscale visual features, thereby enhancing visual perception. Then, a visual-language alignment method is developed for Popeye to enhance interactive comprehension ability between visual and language content. Furthermore, an instruction adaption mechanism is proposed for transferring the pretrained visual-language knowledge from the nature scene into the RS domain for multisource ship detection. In addition, the segment anything model is also seamlessly integrated into the proposed Popeye to achieve pixel-level ship segmentation without additional training costs. Finally, extensive experiments are conducted on the newly constructed ship instruction dataset named MMShip, and the results indicate that the proposed Popeye outperforms current specialist, open-vocabulary, and other visual-language models in zero-shot multisource various ship detection tasks.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20050-20063"},"PeriodicalIF":4.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645478","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}

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