Pub Date : 2025-01-01Epub Date: 2023-07-30DOI: 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
The present study aimed to assess the mediating role of adjustment processes in known risk factors associated with prolonged grief disorder. Data were collected in March-April 2021 through an online survey of 542 Canadian adults bereaved since March 2020. The mediating role of satisfaction with funeral rituals, bereavement support, and coping strategies on grief outcomes was tested using structural equation modeling. Results showed that such adjustment processes played a significant role in the grief process and that they were better predictors than risk factors alone. Since they are more amenable determinants of grief reactions, they should be further studied using a longitudinal design.
{"title":"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.","authors":"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","doi":"10.1177/10541373231191316","DOIUrl":"10.1177/10541373231191316","url":null,"abstract":"<p><p>The present study aimed to assess the mediating role of adjustment processes in known risk factors associated with prolonged grief disorder. Data were collected in March-April 2021 through an online survey of 542 Canadian adults bereaved since March 2020. The mediating role of satisfaction with funeral rituals, bereavement support, and coping strategies on grief outcomes was tested using structural equation modeling. Results showed that such adjustment processes played a significant role in the grief process and that they were better predictors than risk factors alone. Since they are more amenable determinants of grief reactions, they should be further studied using a longitudinal design.</p>","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"14 1","pages":"22-43"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74407375","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}
Pub Date : 2024-11-13DOI: 10.1109/JSTARS.2024.3429949
{"title":"Frontcover","authors":"","doi":"10.1109/JSTARS.2024.3429949","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3429949","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"C1-C1"},"PeriodicalIF":4.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10752091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636301","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}
Pub Date : 2024-11-11DOI: 10.1109/JSTARS.2024.3486922
Yu Shi;Yi Li;Lan Du;Yuang Du;Yuchen Guo
This article proposes an unsupervised domain adaptation (UDA) method by transferring knowledge from rich labeled optical domain to unlabeled synthetic aperture radar (SAR) domain, tackling the issue that current deep-learning-based SAR target detection methods rely on abundant labeled SAR images. Specifically, we gradually encode the dependencies across different granularity perspectives including domain invariant representations (DIR) learning based on feature decomposition and domain discriminative representations (DDR) learning based on uncertainty-guided self-training. First, existing methods usually learn the DIR by directly minimizing domain discrepancy between two domains, which is difficult to achieve in practice. Due to the huge difference between the optical and SAR images, rich domain-specific characteristics bring great challenges to learn the DIR. To alleviate the above difficulty, we explicitly model the domain-invariant and domain-specific features in the representations by constructing a network with feature decomposition to better extract the DIR across domains, where only the DIR extracted from optical images and their labels are used to train the domain-shared detector in this stage. Second, even DIR can be extracted, the domain-shared detector will lose some discriminative and valuable features of the SAR domain while minimizing the distribution discrepancy between the SAR and labeled optical domain. In order to achieve the better detection performance for SAR images, a self-training method based on pseudolabels is proposed to learn DDR and train the SAR-dedicated detector. Furthermore, for ensuring the reliability of pseudolabels, we present a novel uncertainty-guided pseudolabel selection strategy, which contains two phases: one is instance uncertainty guided selection, the other is image uncertainty guided selection. Finally, based on measured optical and SAR datasets, we conduct extensive empirical evaluation to verify the effectuality of our proposed method.
目前基于深度学习的合成孔径雷达（SAR）目标检测方法依赖于丰富的标注合成孔径雷达图像，本文针对这一问题，提出了一种无监督域适应（UDA）方法，将丰富的标注光学域知识转移到非标注合成孔径雷达（SAR）域。具体来说，我们从不同的粒度角度逐步编码依赖关系，包括基于特征分解的域不变表征（DIR）学习和基于不确定性引导的自我训练的域判别表征（DDR）学习。首先，现有方法通常通过直接最小化两个域之间的域差异来学习 DIR，这在实践中很难实现。由于光学图像和合成孔径雷达图像之间存在巨大差异，丰富的特定域特征给 DIR 学习带来了巨大挑战。为了缓解上述困难，我们通过构建一个具有特征分解功能的网络，在表征中明确建立域不变特征和域特定特征模型，以更好地提取跨域的 DIR，在此阶段仅使用从光学图像中提取的 DIR 及其标签来训练域共享检测器。其次，即使能提取出 DIR，域共享检测器也会丢失 SAR 域中一些有鉴别力和有价值的特征，同时最大限度地减少 SAR 和标记光学域之间的分布差异。为了实现更好的 SAR 图像检测性能，本文提出了一种基于伪标签的自训练方法来学习 DDR 并训练 SAR 专用检测器。此外，为确保伪标签的可靠性，我们提出了一种新颖的不确定性引导伪标签选择策略，该策略包含两个阶段：一个是实例不确定性引导选择，另一个是图像不确定性引导选择。最后，基于测量的光学和合成孔径雷达数据集，我们进行了广泛的实证评估，以验证我们提出的方法的有效性。
{"title":"Unsupervised Domain Adaptative SAR Target Detection Based on Feature Decomposition and Uncertainty-Guided Self-Training","authors":"Yu Shi;Yi Li;Lan Du;Yuang Du;Yuchen Guo","doi":"10.1109/JSTARS.2024.3486922","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3486922","url":null,"abstract":"This article proposes an unsupervised domain adaptation (UDA) method by transferring knowledge from rich labeled optical domain to unlabeled synthetic aperture radar (SAR) domain, tackling the issue that current deep-learning-based SAR target detection methods rely on abundant labeled SAR images. Specifically, we gradually encode the dependencies across different granularity perspectives including domain invariant representations (DIR) learning based on feature decomposition and domain discriminative representations (DDR) learning based on uncertainty-guided self-training. First, existing methods usually learn the DIR by directly minimizing domain discrepancy between two domains, which is difficult to achieve in practice. Due to the huge difference between the optical and SAR images, rich domain-specific characteristics bring great challenges to learn the DIR. To alleviate the above difficulty, we explicitly model the domain-invariant and domain-specific features in the representations by constructing a network with feature decomposition to better extract the DIR across domains, where only the DIR extracted from optical images and their labels are used to train the domain-shared detector in this stage. Second, even DIR can be extracted, the domain-shared detector will lose some discriminative and valuable features of the SAR domain while minimizing the distribution discrepancy between the SAR and labeled optical domain. In order to achieve the better detection performance for SAR images, a self-training method based on pseudolabels is proposed to learn DDR and train the SAR-dedicated detector. Furthermore, for ensuring the reliability of pseudolabels, we present a novel uncertainty-guided pseudolabel selection strategy, which contains two phases: one is instance uncertainty guided selection, the other is image uncertainty guided selection. Finally, based on measured optical and SAR datasets, we conduct extensive empirical evaluation to verify the effectuality of our proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20265-20283"},"PeriodicalIF":4.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672123","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}
Pub Date : 2024-11-01DOI: 10.1109/JSTARS.2024.3481444
Yan Zhou;Christopher Grassotti;Quanhua Liu;Shuyan Liu;Yong-Keun Lee
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.
{"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}
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.
{"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}
Pub Date : 2024-10-31DOI: 10.1109/JSTARS.2024.3483992
Caixia Liu;Huabing Huang;John M. Melack;Ye Tian;Jinxiong Jiang;Xiao Fu;Zhiguo Cao;Shaohua Wang
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 2 . 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 3 km 2 , 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.
{"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\u0000<sup>2</sup>\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\u0000<sup>3</sup>\u0000 km\u0000<sup>2</sup>\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}
Pub Date : 2024-10-30DOI: 10.1109/JSTARS.2024.3488034
Wei Zhang;Miaoxin Cai;Tong Zhang;Guoqiang Lei;Yin Zhuang;Xuerui Mao
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.
{"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}
Pub Date : 2024-10-30DOI: 10.1109/JSTARS.2024.3486723
Hang Yu;Chenyang Li;Yuru Guo;Suiping Zhou
Road extraction from images captured via remote sensing is a pivotal task across multiple domains, encompassing urban planning and intelligent transportation systems. In the realm of high-resolution remote sensing, traditional approaches to road extraction confront obstacles pertaining to reduced accuracy and resilience. This study introduces an innovative methodology for road extraction tailored to high-resolution remote sensing data. The devised algorithm integrates a feature enhancement attention module alongside parallel feature fusion. Specifically, the feature enhancement attention module is introduced to augment the network's capacity in discerning road-related information by analyzing feature maps produced at varying resolutions. Subsequently, during feature map extraction, the parallel feature fusion technique is employed to merge shallow and deep features sharing the same resolution, thus effectively leveraging the strengths of both to enhance the model's precision. Moreover, the network undertakes the computation of correlations among feature maps of differing resolutions as well as the entire feature map, thereby facilitating a holistic grasp of the global structure and semantic information embedded within the image. Experimental evaluations conducted on the CHN6-CUG and Massachusetts datasets substantiate that the proposed approach outperforms prevailing mainstream methods for road extraction in terms of both accuracy and processing speed.
{"title":"Feature Enhancement Attention for Road Extraction in High-Resolution Remote Sensing Image","authors":"Hang Yu;Chenyang Li;Yuru Guo;Suiping Zhou","doi":"10.1109/JSTARS.2024.3486723","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3486723","url":null,"abstract":"Road extraction from images captured via remote sensing is a pivotal task across multiple domains, encompassing urban planning and intelligent transportation systems. In the realm of high-resolution remote sensing, traditional approaches to road extraction confront obstacles pertaining to reduced accuracy and resilience. This study introduces an innovative methodology for road extraction tailored to high-resolution remote sensing data. The devised algorithm integrates a feature enhancement attention module alongside parallel feature fusion. Specifically, the feature enhancement attention module is introduced to augment the network's capacity in discerning road-related information by analyzing feature maps produced at varying resolutions. Subsequently, during feature map extraction, the parallel feature fusion technique is employed to merge shallow and deep features sharing the same resolution, thus effectively leveraging the strengths of both to enhance the model's precision. Moreover, the network undertakes the computation of correlations among feature maps of differing resolutions as well as the entire feature map, thereby facilitating a holistic grasp of the global structure and semantic information embedded within the image. Experimental evaluations conducted on the CHN6-CUG and Massachusetts datasets substantiate that the proposed approach outperforms prevailing mainstream methods for road extraction in terms of both accuracy and processing speed.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"19805-19816"},"PeriodicalIF":4.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636567","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}
Pub Date : 2024-10-30DOI: 10.1109/JSTARS.2024.3488056
Bingshu Wang;Yuhao Xing;Ning Wang;C. L. Philip Chen
The rapid pace of urbanization underscores the importance of waste monitoring and management in urban planning and environmental conservation. Remote sensing technology enables the aerial observation of terrestrial and marine features, with high-resolution images revealing diverse objects. Deep learning techniques have gained prominence for enhancing waste monitoring precision and efficiency. This article surveys deep learning approaches for waste monitoring in remote sensing images, focusing on relevant datasets. It reviews existing remote sensing datasets, including those from uncrewed aerial vehicles and satellites, for monitoring solid waste and marine debris. Nine publicly available datasets are described in detail, highlighting their origins and applications. The monitoring methods include two kinds of methods: 1) semantic segmentation; and 2) object detection. Semantic segmentation focuses on pixel-level classification and boundary delineation, while object detection targets object-level localization and shape. Representative methods within these categories are explored, and benchmark results from recent studies are summarized to evaluate the performance of various techniques. The discussion addresses current limitations and suggests future research directions, aiming to assist researchers and professionals in environmental monitoring.
{"title":"Monitoring Waste From Uncrewed Aerial Vehicles and Satellite Imagery Using Deep Learning Techniques: A Review","authors":"Bingshu Wang;Yuhao Xing;Ning Wang;C. L. Philip Chen","doi":"10.1109/JSTARS.2024.3488056","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3488056","url":null,"abstract":"The rapid pace of urbanization underscores the importance of waste monitoring and management in urban planning and environmental conservation. Remote sensing technology enables the aerial observation of terrestrial and marine features, with high-resolution images revealing diverse objects. Deep learning techniques have gained prominence for enhancing waste monitoring precision and efficiency. This article surveys deep learning approaches for waste monitoring in remote sensing images, focusing on relevant datasets. It reviews existing remote sensing datasets, including those from uncrewed aerial vehicles and satellites, for monitoring solid waste and marine debris. Nine publicly available datasets are described in detail, highlighting their origins and applications. The monitoring methods include two kinds of methods: 1) semantic segmentation; and 2) object detection. Semantic segmentation focuses on pixel-level classification and boundary delineation, while object detection targets object-level localization and shape. Representative methods within these categories are explored, and benchmark results from recent studies are summarized to evaluate the performance of various techniques. The discussion addresses current limitations and suggests future research directions, aiming to assist researchers and professionals in environmental monitoring.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20064-20079"},"PeriodicalIF":4.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645538","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}
Pub Date : 2024-10-29DOI: 10.1109/JSTARS.2024.3487584
Kuifeng Luan;Xueyan Zhao;Wei Kong;Tao Chen;Huan Xie;Xiangfeng Liu;Fengxiang Wang
Unmanned aerial vehicle-based photon-counting ocean bathymetric light detection and ranging (LiDAR) systems rapidly acquire topographic data from islands, reefs, and shallow waters. However, the bathymetric ability of seabed topography is affected by high backscattering from the sea surface owing to its proximity, and no suitable imaging models are available. Herein, we designed a novel ray approach for bathymetry based on a dual-polarization photon-counting LiDAR. Based on the transmission characteristics of light, a dual-polarization channel strategy was proposed, and the data from two channels in vegetation, sand, and shallow and medium-depth waters were compared. Based on the ray tracing method, imaging models of the water surface and depth for light and small photon-counting LiDAR were established. Shallow-water experiments were conducted near Jiajing Island, Hainan, China, to verify the accuracy of the LiDAR bathymetry data by shipborne single-beam sounding data. The results indicate that the vertical polarization channel data had a high signal-to-noise ratio in the terrestrial part, while the horizontal polarization channel had a better water surface backscatter suppression effect and strong bathymetry ability in the water part. The detectable depth was approximately 8 m in the experimental area. The MAEs of the depth values of the LiDAR point cloud before and after refraction correction relative to the single beam depth measurement values were 1.08 and 0.09 m, respectively. And the RMSEs before and after correction were 1.12 and 0.11 m, respectively.
{"title":"A Novel Ray Tracing Approach for Bathymetry Using UAV-Based Dual-Polarization Photon-Counting LiDAR","authors":"Kuifeng Luan;Xueyan Zhao;Wei Kong;Tao Chen;Huan Xie;Xiangfeng Liu;Fengxiang Wang","doi":"10.1109/JSTARS.2024.3487584","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3487584","url":null,"abstract":"Unmanned aerial vehicle-based photon-counting ocean bathymetric light detection and ranging (LiDAR) systems rapidly acquire topographic data from islands, reefs, and shallow waters. However, the bathymetric ability of seabed topography is affected by high backscattering from the sea surface owing to its proximity, and no suitable imaging models are available. Herein, we designed a novel ray approach for bathymetry based on a dual-polarization photon-counting LiDAR. Based on the transmission characteristics of light, a dual-polarization channel strategy was proposed, and the data from two channels in vegetation, sand, and shallow and medium-depth waters were compared. Based on the ray tracing method, imaging models of the water surface and depth for light and small photon-counting LiDAR were established. Shallow-water experiments were conducted near Jiajing Island, Hainan, China, to verify the accuracy of the LiDAR bathymetry data by shipborne single-beam sounding data. The results indicate that the vertical polarization channel data had a high signal-to-noise ratio in the terrestrial part, while the horizontal polarization channel had a better water surface backscatter suppression effect and strong bathymetry ability in the water part. The detectable depth was approximately 8 m in the experimental area. The MAEs of the depth values of the LiDAR point cloud before and after refraction correction relative to the single beam depth measurement values were 1.08 and 0.09 m, respectively. And the RMSEs before and after correction were 1.12 and 0.11 m, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"20284-20303"},"PeriodicalIF":4.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737662","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672184","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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