A spectral-spatial fusion anomaly detection method for hyperspectral imagery

TL;DR

This paper introduces a spectral-spatial fusion method for hyperspectral anomaly detection that combines spectral and spatial features to improve detection accuracy, outperforming traditional approaches.

Contribution

The paper proposes a novel spectral-spatial fusion anomaly detection method that adaptively combines spectral and spatial information for hyperspectral imagery.

Findings

Superior detection performance over traditional methods

Effective use of local background similarity information

Enhanced anomaly detection accuracy

Abstract

In hyperspectral, high-quality spectral signals convey subtle spectral differences to distinguish similar materials, thereby providing unique advantage for anomaly detection. Hence fine spectra of anomalous pixels can be effectively screened out from heterogeneous background pixels. Since the same materials have similar characteristics in spatial and spectral dimension, detection performance can be significantly enhanced by jointing spatial and spectral information. In this paper, a spectralspatial fusion anomaly detection (SSFAD) method is proposed for hyperspectral imagery. First, original spectral signals are mapped to a local linear background space composed of median and mean with high confidence, where saliency weight and feature enhancement strategies are implemented to obtain an initial detection map in spectral domain. Futhermore, to make full use of similarity information of local background around testing pixel, a new detector is designed to extract the local similarity spatial features of patch images in spatial domain. Finally, anomalies are detected by adaptively combining the spectral and spatial detection maps. The experimental results demonstrate that our proposed method has superior detection performance than traditional methods.