VcT: Visual change Transformer for Remote Sensing Image Change Detection
Bo Jiang, Zitian Wang, Xixi Wang, Ziyan Zhang, Lan Chen, Xiao Wang,, Bin Luo

TL;DR
This paper introduces VcT, a novel Transformer-based model that leverages background context and structured graph modeling to improve remote sensing image change detection accuracy.
Contribution
The paper proposes a new visual change Transformer (VcT) that mines background information and uses graph neural networks for enhanced change detection.
Findings
Outperforms existing methods on benchmark datasets
Effectively mines reliable tokens for change map refinement
Improves consistency in representations across varied conditions
Abstract
Existing visual change detectors usually adopt CNNs or Transformers for feature representation learning and focus on learning effective representation for the changed regions between images. Although good performance can be obtained by enhancing the features of the change regions, however, these works are still limited mainly due to the ignorance of mining the unchanged background context information. It is known that one main challenge for change detection is how to obtain the consistent representations for two images involving different variations, such as spatial variation, sunlight intensity, etc. In this work, we demonstrate that carefully mining the common background information provides an important cue to learn the consistent representations for the two images which thus obviously facilitates the visual change detection problem. Based on this observation, we propose a novel…
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Taxonomy
TopicsRemote-Sensing Image Classification · Remote Sensing and Land Use
MethodsMulti-Head Attention · Attention Is All You Need · Dense Connections · Linear Layer · Softmax · Residual Connection · Absolute Position Encodings · Layer Normalization · Adam · Byte Pair Encoding
