Multi-class change detection using a multi-task Siamese network of remote sensing images

doi:10.6046/zrzyyg.2022446

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Abstract

The accurate acquisition of land cover/use changes and their types is critical to territorial space planning, ecological environment monitoring, and disaster assessment. However, most current studies on the change detection focus on binary change detection. This study proposed a multi-class change detection method using a multi-task Siamese network of remote sensing images. First, an object-oriented unsupervised change detection method was employed to select areas that were most/least prone to change in the new and old temporal images. These areas were used as samples for the multi-task Siamese network. Subsequently, the multi-task Siamese network model was used to learn and predict the new and old temporal land-use maps and binary change maps. Finally, the final multi-class change detection results were derived from these maps. The multi-task Siamese network was tested based on the images from the Third National Land Survey and corresponding land-use maps. The results demonstrate that the method proposed in this study is applicable to the change detection cases where changed and unchanged samples lack but there are available historical thematic maps.

Keywords multi-task learning Siamese network multi-class change detection the third national land resource survey

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TP751

Issue Date: 13 March 2024

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	Hui MA
	Bo LIU
	Shihong DU

Cite this article:

Hui MA,Bo LIU,Shihong DU. Multi-class change detection using a multi-task Siamese network of remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(1): 77-85.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022446 OR https://www.gtzyyg.com/EN/Y2024/V36/I1/77

Fig.1 Data used in this paper

Fig.2 Change detection workflow of remote sensing images

Fig.3 Object-oriented unsupervised change detection workflow

Fig.4 Structure of multi-task learning Siamese network

Fig.5 Structure of Xception network

变化轨迹		样本/未变化					样本/变化
变化轨迹		$L 11$ → $L 12$	…	$L 41$ → $L 42$	…	$L m 1$ → $L m 2$	$L 11$ → $L 22$	…	$L 41$ → $L 52$	…	$L m - 1 1$ → $L m 2$
检测结果/未变化	$L 11$ → $L 12$	S₁	S₃	S₃	S₃	S₃	S₅	S₅	S₅	S₅	S₅
	…	S₃	S₁	S₃	S₃	S₃	S₅	S₅	S₅	S₅	S₅
	$L 41$ → $L 42$	S₃	S₃	S₁	S₃	S₃	S₅	S₅	S₅	S₅	S₅
	…	S₃	S₃	S₃	S₁	S₃	S₅	S₅	S₅	S₅	S₅
	$L m 1$ → $L m 2$	S₃	S₃	S₃	S₃	S₁	S₅	S₅	S₅	S₅	S₅
检测结果/变化	$L 11$ → $L 22$	S₄	S₄	S₄	S₄	S₄	S₂	S₆	S₆	S₆	S₆
	…	S₄	S₄	S₄	S₄	S₄	S₆	S₂	S₆	S₆	S₆
	$L 41$ → $L 52$	S₄	S₄	S₄	S₄	S₄	S₆	S₆	S₂	S₆	S₆
	…	S₄	S₄	S₄	S₄	S₄	S₆	S₆	S₆	S₂	S₆
	$L m - 1 1$ → $L m 2$	S₄	S₄	S₄	S₄	S₄	S₆	S₆	S₆	S₆	S₂

Tab.1 Trajectory error matrix of land use and its grouping

Tab.2 Hardware configuration of deep learning platform

Tab.3 Results of unsupervised change detection

Tab.4 Accuracy assessment results of unsupervised change detection (%)

Tab.5 Results of multi-class change detection

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