ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction

遥感 地理 变更检测 地图学 高分辨率 计算机科学
作者
Zhipan Wang,Man Xu,Zhongwu Wang,Huadong Guo,Qingling Zhang
出处
期刊:International journal of applied earth observation and geoinformation 卷期号:128: 103761-103761
标识
DOI:10.1016/j.jag.2024.103761
摘要

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.
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