TY - JOUR
T1 - Cross-Resolution Change Detection in Remote Sensing via Unequal Relationships From a Frequency Perspective
AU - Ning, Lichen
AU - Zhou, Qing
AU - Wang, Qi
AU - Gao, Junyu
AU - Li, Xuelong
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - 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 F1 /intersection over union (IoU) improvements of 2.15/3.51, 2.50/4.59, and 1.93/1.73 on the LEVIR-CD ( 4x), SV-CD (8x), and DE-CD (3.3x) tests, respectively. Furthermore, in the continuous CRCD task, FWISN achieves F1 /IoU improvements of 8.39/11.24 on the LEVIR-CD (8x) test.
AB - 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 F1 /intersection over union (IoU) improvements of 2.15/3.51, 2.50/4.59, and 1.93/1.73 on the LEVIR-CD ( 4x), SV-CD (8x), and DE-CD (3.3x) tests, respectively. Furthermore, in the continuous CRCD task, FWISN achieves F1 /IoU improvements of 8.39/11.24 on the LEVIR-CD (8x) test.
KW - Cross-resolution change detection (CRCD)
KW - frequency-domain learning
KW - remote sensing
UR - https://www.scopus.com/pages/publications/105011067648
U2 - 10.1109/TGRS.2025.3590023
DO - 10.1109/TGRS.2025.3590023
M3 - 文章
AN - SCOPUS:105011067648
SN - 0196-2892
VL - 63
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 4414514
ER -