ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction
Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a v...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224001158 |
| _version_ | 1797224318819631104 |
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| author | Zhipan Wang Minduan Xu Zhongwu Wang Qing Guo Qingling Zhang |
| author_facet | Zhipan Wang Minduan Xu Zhongwu Wang Qing Guo Qingling Zhang |
| author_sort | Zhipan Wang |
| collection | DOAJ |
| description | Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a valuable technique for enhancing model performance with minimal human interaction. For instance, the clicks-based interactive models have attracted much attention recently, however, their performance on large regions or complex areas still can be further improved, because they cannot provide accurate semantics or shape prior information of the change regions for the interactive models, as we know that the shape and semantic features of changed regions in remote sensing imagery are typically irregular and complex. Scribble-based interactive form, which can accurately represent the shape or semantic features of the changed regions, thus it is quite suitable for change detection tasks in remote sensing imagery. Therefore, we proposed a novel interactive deep learning model called ScribbleCDNet in this manuscript, which pioneered the use of scribble as an interactive form for detecting change in bi-temporal high-resolution remote sensing imageries. Compared with the widely used clicks-based interactive deep learning models, the proposed ScribbleCDNet acquired superior results on four open-sourced change detection datasets. Last but not least, we also developed an interactive change detection tool with a user-friendly graphical interface, and it can aid researchers in conducting change detection or generating training samples conveniently. Moreover, the proposed ScribbleCDNet can also inspire researchers to develop other interactive deep-learning models related to semantic segmentation, landcover classification, or object extraction in high-resolution remote sensing imageries. |
| first_indexed | 2024-04-24T13:51:13Z |
| format | Article |
| id | doaj.art-27a6991ddd754c15a8dd3258878462d5 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-24T13:51:13Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-27a6991ddd754c15a8dd3258878462d52024-04-04T05:03:47ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-04-01128103761ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interactionZhipan Wang0Minduan Xu1Zhongwu Wang2Qing Guo3Qingling Zhang4Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen, Guangdong 518107, PR ChinaShenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen, Guangdong 518107, PR ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, PR ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, PR ChinaShenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen, Guangdong 518107, PR China; Corresponding author.Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a valuable technique for enhancing model performance with minimal human interaction. For instance, the clicks-based interactive models have attracted much attention recently, however, their performance on large regions or complex areas still can be further improved, because they cannot provide accurate semantics or shape prior information of the change regions for the interactive models, as we know that the shape and semantic features of changed regions in remote sensing imagery are typically irregular and complex. Scribble-based interactive form, which can accurately represent the shape or semantic features of the changed regions, thus it is quite suitable for change detection tasks in remote sensing imagery. Therefore, we proposed a novel interactive deep learning model called ScribbleCDNet in this manuscript, which pioneered the use of scribble as an interactive form for detecting change in bi-temporal high-resolution remote sensing imageries. Compared with the widely used clicks-based interactive deep learning models, the proposed ScribbleCDNet acquired superior results on four open-sourced change detection datasets. Last but not least, we also developed an interactive change detection tool with a user-friendly graphical interface, and it can aid researchers in conducting change detection or generating training samples conveniently. Moreover, the proposed ScribbleCDNet can also inspire researchers to develop other interactive deep-learning models related to semantic segmentation, landcover classification, or object extraction in high-resolution remote sensing imageries.http://www.sciencedirect.com/science/article/pii/S1569843224001158Change detectionInteractive deep learningScribble interactionHigh-resolution remote sensing imagery |
| spellingShingle | Zhipan Wang Minduan Xu Zhongwu Wang Qing Guo Qingling Zhang ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction International Journal of Applied Earth Observations and Geoinformation Change detection Interactive deep learning Scribble interaction High-resolution remote sensing imagery |
| title | ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction |
| title_full | ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction |
| title_fullStr | ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction |
| title_full_unstemmed | ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction |
| title_short | ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction |
| title_sort | scribblecdnet change detection on high resolution remote sensing imagery with scribble interaction |
| topic | Change detection Interactive deep learning Scribble interaction High-resolution remote sensing imagery |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224001158 |
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