Rethinking Remote Sensing Change Detection With A Mask View

• URL: http://arxiv.org/abs/2406.15320v1
• Date: Fri, 21 Jun 2024 17:27:58 GMT
• Title: Rethinking Remote Sensing Change Detection With A Mask View
• Authors: Xiaowen Ma, Zhenkai Wu, Rongrong Lian, Wei Zhang, Siyang Song,
• Abstract summary: Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different stamps time to assess changes in geographical entities and environmental factors. To address this shortcoming, this paper rethinks the change detection with the mask view, and further proposes the corresponding: 1) meta-architecture CDMask and 2) instance network CDMaskFormer.
• Score: 6.3921187411592655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote sensing change detection aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and qualitatively assess changes in geographical entities and environmental factors. Mainstream models usually built on pixel-by-pixel change detection paradigms, which cannot tolerate the diversity of changes due to complex scenes and variation in imaging conditions. To address this shortcoming, this paper rethinks the change detection with the mask view, and further proposes the corresponding: 1) meta-architecture CDMask and 2) instance network CDMaskFormer. Components of CDMask include Siamese backbone, change extractor, pixel decoder, transformer decoder and normalized detector, which ensures the proper functioning of the mask detection paradigm. Since the change query can be adaptively updated based on the bi-temporal feature content, the proposed CDMask can adapt to different latent data distributions, thus accurately identifying regions of interest changes in complex scenarios. Consequently, we further propose the instance network CDMaskFormer customized for the change detection task, which includes: (i) a Spatial-temporal convolutional attention-based instantiated change extractor to capture spatio-temporal context simultaneously with lightweight operations; and (ii) a scene-guided axial attention-instantiated transformer decoder to extract more spatial details. State-of-the-art performance of CDMaskFormer is achieved on five benchmark datasets with a satisfactory efficiency-accuracy trade-off. Code is available at https://github.com/xwmaxwma/rschange.

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