Hyret-Change: A Hybrid Retentive Network for Remote Sensing Change Detection
Fiaz, Mustansar Noman, Mubashir Debary, Hiyam Ali, Kamran Cholakkal, Hisham
Fiaz, Mustansar
Noman, Mubashir
Debary, Hiyam
Ali, Kamran
Cholakkal, Hisham
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Computer Vision
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Conference proceeding
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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.
Citation
M. Fiaz, M. Noman, H. Debary, K. Ali, H. Cholakkal, "Hyret-Change: A Hybrid Retentive Network for Remote Sensing Change Detection," 2025, pp. 7209-7213.
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Conference
IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium
Keywords
46 Information and Computing Sciences, 4611 Machine Learning
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Source
IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium
Publisher
IEEE