|国家预印本平台
首页|HyRet-Change: A hybrid retentive network for remote sensing change detection

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

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

来源:Arxiv_logoArxiv
英文摘要

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.

Mustansar Fiaz、Mubashir Noman、Hiyam Debary、Kamran Ali、Hisham Cholakkal

遥感技术

Mustansar Fiaz,Mubashir Noman,Hiyam Debary,Kamran Ali,Hisham Cholakkal.HyRet-Change: A hybrid retentive network for remote sensing change detection[EB/OL].(2025-06-15)[2025-07-23].https://arxiv.org/abs/2506.12836.点此复制

评论