Work

Wavelet-based diffusion with spatial-frequency attention for hyperspectral anomaly detection

Year: 2025

Type: article

Abstract: Frequency decomposition offers a promising approach for hyperspectral anomaly detection (HAD) by separating anomalies from redundant backgrounds. However, an improper decomposition strategy may cause domain shifts in the low-frequency component (LFC) and excessive suppression of the high-frequency component (HFC), ultimately affecting detection performance. To address those challenges, we propose a novel frequency decomposition framework wavelet-enhanced diffusion framework for HAD, termed as WDHAD. Following a 2D discrete wavelet transformation, the LFC and HFC are processed in parallel: 1) The LFC is handled via a Low-Frequency Diffusion Model (LFDM), which employs a Low-Frequency Denoising Autoencoder (LFDAE) with spatial-frequency attention to recover key features and remove background noise. 2) The HFC is processed through a High-Frequency Enhancement Module (HFEM) that preserves edges and textures to improve anomaly detection. Both components are then fused and passed through a 2D inverse wavelet transformation, with the detection map obtained by a Reed-Xiaoli detector. In addition, a negative log-likelihood noise loss is introduced to model uncertainty. Extensive experiments on six public and two real-world UAV datasets demonstrate that WDHAD achieves robust generalization and cross-domain adaptability. The code will be publicly available at https://github.com/CZhu0066/WDHAD . • This work proposes a wavelet-based diffusion model for hyperspectral anomaly detection. • This work combines the spatial and frequency dual-domain features. • This work introduces the negative log-likelihood noise loss function. • Extensive experiments validate robustness and cross-domain adaptability.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104662

Authors: Sitian Liu, Chunli Zhu, Lintao Peng, Xinyue Su, Lianjie Li, Guanghui Wen

Institutions: Beijing Institute of Technology, Southeast University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,550

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, National Key Research and Development Program of China Stem Cell and Translational Research