Multiple spatial and wavelet feature interaction learning for change detection in remote sensing images
Year: 2025
Type: article
Abstract: Change detection (CD) in remote sensing (RS) images is essential for earth observation, monitoring dynamic environmental and urban changes. Existing deep learning (DL)-based methods often struggle with pseudochanges and subtle small-scale changes. To address this, we propose a spatial and wavelet interactive learning (SWIL) framework, which jointly optimizes spatial and spectral feature learning. By leveraging multidomain feature complementarity, SWIL suppresses pseudochanges while enhancing fine-scale detection, improving noise robustness. Specifically, we integrate the wavelet transform into feature filtering, exploiting its multiscale geometric analysis for fine-grained frequency-domain representation. Our spatial and wavelet feature enhancement (SWE) module decomposes and aggregates multiscale features with local convolutions, effectively reducing pseudo-change noise. Additionally, the Channel Selective Multiscale Filtering (CSF) module refines spatial-frequency features, improving small-change detection in complex scenes. Experiments on three benchmarks confirm SWIL's superiority over state-of-the-art methods, achieving higher accuracy and noise resilience under complex conditions.
Source: Geocarto International
Authors: Zhaoqian Wang, Wei Yang, YuXi Su, Xue Ding, Ying Wang
Institutions: Yunnan Normal University, Wuchang Shouyi University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Funder: Yunnan Normal University
Award: G3802234125
OpenAlex ID: W4416572272