Interaction in Transformer for Change Detection in VHR Remote Sensing Images | IEEE Journals & Magazine | IEEE Xplore

Interaction in Transformer for Change Detection in VHR Remote Sensing Images


Abstract:

With the development of deep learning (DL), very high-resolution remote sensing (RS) image change detection (VHRCD) methods are becoming more popular. However, most exist...Show More

Abstract:

With the development of deep learning (DL), very high-resolution remote sensing (RS) image change detection (VHRCD) methods are becoming more popular. However, most existing change detection methods are not good at processing edge details and small target detection. To this end, in this article, InterFormer, a bidirectional interactive framework based on Transformer, is proposed to find small changes and extract more accurate edge information of the change area. First, we designed an asymmetric interaction attention module (IAM) to identify the edge details for the bitemporal image. The IAM fully leverages the benefits of self-attention, performing feature fusion during feature extraction. This approach improves edge feature extraction capability and reduces the number of parameters, compared to other Transformer methods. Second, we designed a global attention-based feature fusion module called global feature fusion module (GFFM) to enhance the detection performance of small targets. The GFFM further improves small target detection ability by augmenting the network’s selectivity to spatial information during feature fusion. The method applies to scenarios involving small changes and possesses enhanced edge-detection capabilities. Our method outperforms state-of-the-art counterparts on three public benchmarks and has fewer parameters.
Article Sequence Number: 3000612
Date of Publication: 13 October 2023

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