SFFCE-CD: Spatial And Frequency Feature Cross Enhancement For Change Detection

Most existing change detection (CD) methods focus on spatial domain modeling, while ignore the rich information of frequency domain. In this paper, we enhance the feature representative ability with spatial and frequency feature cross enhancement. Specifically, we propose a Change Feature Extract Module (CFEM) to obtain high-quality change features from the bi-temporal images. These features are then merged together and refined through three parallel branches: the Local branch uses multiscale max-pooling operations to generate multiscale feature; the Wavelet Transform Decomposer (WTD) branch decomposes feature into low-frequency and high-frequency signals with Haar wavelet transform; the Global branch adopts Mamba to capture the long-range dependencies. To bridge the semantic gap between frequency and spatial features, we design Dual-Representation Aggregation Module (DRAM) to promote the combination of features from different representation domains in a flow-based manner and dual cross attention. Extensive experiments demonstrate our method outperforms 11 SOTA CD methods on three remote sensing CD datasets.