HyRet-Change: A hybrid retentive network for remote sensing change detection

TL;DR

HyRet-Change introduces a hybrid network combining convolution and retention mechanisms for improved remote sensing change detection, effectively capturing local and global dependencies and achieving state-of-the-art results.

Contribution

The paper presents a novel Siamese framework integrating convolution and retention mechanisms with a feature difference module and adaptive context awareness for enhanced change detection.

Findings

Achieves state-of-the-art performance on three challenging datasets.

Effectively captures subtle changes with improved discrimination.

Demonstrates robustness in complex scenes.

Abstract

Recently convolution and transformer-based change detection (CD) methods provide promising performance. However, it remains unclear how the local and global dependencies interact to effectively alleviate the pseudo changes. Moreover, directly utilizing standard self-attention presents intrinsic limitations including governing global feature representations limit to capture subtle changes, quadratic complexity, and restricted training parallelism. To address these limitations, we propose a Siamese-based framework, called HyRet-Change, which can seamlessly integrate the merits of convolution and retention mechanisms at multi-scale features to preserve critical information and enhance adaptability in complex scenes. Specifically, we introduce a novel feature difference module to exploit both convolutions and multi-head retention mechanisms in a parallel manner to capture complementary information. Furthermore, we propose an adaptive local-global interactive context awareness mechanism that enables mutual learning and enhances discrimination capability through information exchange. We perform experiments on three challenging CD datasets and achieve state-of-the-art performance compared to existing methods. Our source code is publicly available at https://github.com/mustansarfiaz/HyRect-Change.