An Adaptive Weighted Metric Learning Network Based on Fractional Domain Decoupling for Hyperspectral Change Detection

Hyperspectral image change detection (HSI-CD) possesses strong capabilities in exploring subtle changes in land cover. Due to sensor noise and imaging conditions, different semantic land covers in the same spatial location may exhibit similar spectral characteristics, leading to pseudoinvariant phenomena (identification of changed areas as unchanged areas) and causing a higher rate of false negatives in the model. Existing methods primarily focus on obtaining auxiliary discriminative information from spatial correlations or temporal dependencies. However, the frequency domain, which possesses rich global gradient distribution information, is often overlooked. The fractional Fourier transform (FrFT) is an extension of the Fourier transform (FT), representing a temporal-frequency local transformation suitable for processing nonstationary signals. Furthermore, multiorder fractional Fourier domains provide more observable domains for change discrimination. In this work, the application of FrFT is extended to the field of HSI-CD, and an adaptive weighted metric learning network based on fractional domain decoupling (FrFTML) is proposed. Specifically, the fractional domain decoupling (FrDD) module transforms the original HSI into multiorder FrFT domains and extracts their rich spatial-frequency mixed information, effectively suppressing noise while enhancing the representation of subtle differences. In addition, an adaptive weighted metric learning (AWML) framework is designed to merge multiorder fractional Fourier domain information in an adaptively weighted fusion manner. It introduces deep metric learning to explore the distances between samples of different categories that have relatively high similarity, so as to guide the direction of adaptive weighted fusion. Finally, the differential mask attention (DMA) module is designed to explore global contextual differences between bitemporal HSIs, obtaining change features with well-represented differences. Some experiments conducted on three public datasets indicate that FrFTML outperforms other state-of-the-art methods. Furthermore, the proposed method exhibits superiority in dealing with land cover that may lead to pseudoinvariant phenomena (identification of changed areas as unchanged areas).