Work

A Balanced Spectral–Spatial Cross-Fusion Network for Hyperspectral Anomaly Detection

Year: 2026

Type: article

Abstract: Hyperspectral anomaly detection aims to find abnormal targets without prior information. However, the high dimensionality of hyperspectral data, complicated spatial structures, and varying object scales make it challenging to jointly utilize spectral and spatial information. Therefore, anomalies may be confused with background regions. A Balanced Spectral–Spatial Cross-Fusion Network (BSCF-Net) is proposed for hyperspectral anomaly detection. The network uses a multi-branch encoder, where spatial branches capture features with different receptive fields and spectral branches extract spectral patterns through one-dimensional convolutions and channel attention. The Bidirectional Spectral–Spatial Cross-Attention (BSCA) mechanism enables information exchange between spectral and spatial features. The Multi-Scale Gated Refiner (MSGR) module is used to refine the fused features. With an autoencoder reconstruction framework, BSCF-Net identifies anomalies according to reconstruction errors and reduces background interference. Experimental results on five public hyperspectral datasets demonstrate the effectiveness of BSCF-Net, achieving competitive AUC performance under diverse background conditions.

Source: Remote Sensing

DOI: 10.3390/rs18162820

Authors: Yuquan Gan, Mengjiao Wang, Lei Zhang, Weidong Zhang, Tao Yu, Hongwei Wang

Institutions: Xi’an University of Posts and Telecommunications, Chinese Academy of Sciences, Xi'an Institute of Optics and Precision Mechanics, Northwestern Polytechnical University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funder: National Natural Science Foundation of China