Cross-Resolution Change Detection in Remote Sensing via Unequal Relationships From a Frequency Perspective
Cross-resolution change detection (CRCD) identifies changes between bitemporal images with different resolutions, which provide better adaptability to real-world applications than conventional change detection (CD). Existing CRCD methods first align the resolution of different temporal images, then employ the Siamese network. Experiments conducted from a frequency-domain perspective validate that resize operations disrupt the data distribution and result in performance degradation of the Siamese network. Further experiments reveal the resolution-invariant temporal and spatial unequal relationship between bitemporal images. Specifically, spatial specificity information within a specific temporal domain is more critical for CRCD, i.e., high-frequency components in a specific temporal domain are closely related to change label. And this unequal relationship exhibits invariance in resolution. On this basis, we propose the Fourier and wavelet transform-based inequality Siamese network (FWISN) to address the performance degradation observed in Siamese networks on CRCD, leveraging the inequality between bitemporal images to improve network performance. FWISN includes a frequency reconstruction (FRC) stage, in which high-frequency components of a given temporal-domain image are extracted and reconstructed using our proposed high-frequency attention (HFA) module. We further propose the wavelet transform-based frequency learning block (WFB), which enhances high-frequency features and is integrated into both the encoder (WFB-E) and the decoder (WFB-D) of the network. The experiments demonstrate state-of-the-art performance, compared with methods specifically designed for cross-resolution tasks, FWISN achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>/intersection over union (IoU) improvements of 2.15/3.51, 2.50/4.59, and 1.93/1.73 on the LEVIR-CD (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times $ </tex-math></inline-formula>), SV-CD (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 \times $ </tex-math></inline-formula>), and DE-CD (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3\times $ </tex-math></inline-formula>) tests, respectively. Furthermore, in the continuous CRCD task, FWISN achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula>/IoU improvements of 8.39/11.24 on the LEVIR-CD (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8 \times $ </tex-math></inline-formula>) test. Our code will be public on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/blacksheep182/NN_FWISN</uri>
Authors
- Junyu Gao (ORCID: https://orcid.org/0000-0001-6000-8168)
- Qing Zhou (ORCID: https://orcid.org/0009-0004-3300-0805)
- Qi Wang (ORCID: https://orcid.org/0000-0002-7028-4956)
- Liu Ning (ORCID: https://orcid.org/0000-0002-2144-9478)
- Xuelong Li (ORCID: https://orcid.org/0000-0003-2924-946X)
Institutions
- Northwestern Polytechnical University (CN)
- China Telecom (China) (CN)
- China Telecom (CN)
Publication Details
- Journal
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tgrs.2025.3590023
- Citations
- 2
- Primary Topic
- Remote-Sensing Image Classification
- Type
- article
- Field-Weighted Citation Impact
- 2.27
Funders
- National Science Fund for Distinguished Young Scholars