EGMT-CD: Edge-Guided Multimodal Transformers Change Detection from Satellite and Aerial Images

作者： Yunfan   Xiang     Xiangyu   Tian     Yue   Xu     Xiaokun   Guan     Zhengchao   Chen   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： sensing   detection   images   edge   Transformer   feature   change   remote   alignment   heterogeneous  

描述： Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images.

EGMT-CD: Edge-Guided Multimodal Transformers Change Detection from Satellite and Aerial Images

作者： Yunfan   Xiang     Xiangyu   Tian     Yue   Xu     Xiaokun   Guan     Zhengchao   Chen   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： sensing   detection   images   edge   Transformer   feature   change   remote   alignment   heterogeneous  

描述： Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images.

Cloud-inclusive Aerial Imagery based on Commercial Flights as Remote Sensing Platform

作者： Zidic   Dinko     Culic   Gambiroza   Jelena     Mastelic   Toni     Cagalj   Mario   来源： Journal of Communications Software and Systems 年份： 2023 文献类型 ： 期刊 关键词： coverage   Commercial   sensing   flight   inclusive   Cloud   imagery   clouds   remote   aerial   flights  

描述： to advance rapidly, with remote sensing being a key component for gathering Earth-related

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： vegetation type classification, especially using high-spatial-resolution remote sensing images. T

Cloud-inclusive Aerial Imagery based on Commercial Flights as Remote Sensing Platform

作者： Zidic   Dinko     Culic   Gambiroza   Jelena     Mastelic   Toni     Cagalj   Mario   来源： Journal of Communications Software and Systems 年份： 2023 文献类型 ： 期刊 关键词： coverage   Commercial   sensing   flight   inclusive   Cloud   imagery   clouds   remote   aerial   flights  

描述： to advance rapidly, with remote sensing being a key component for gathering Earth-related

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： vegetation type classification, especially using high-spatial-resolution remote sensing images. T