GAN-based Hyperspectral Anomaly Detection

TL;DR

This paper introduces a GAN-based method for hyperspectral anomaly detection that generates synthetic backgrounds to improve detection accuracy, outperforming classical and deep learning methods on synthetic and real data.

Contribution

The paper presents a novel GAN-based background modeling approach for hyperspectral anomaly detection, enhancing detection performance over existing methods.

Findings

Outperforms classical RX, WRX, SVDD, and DAEAD methods.

Effective on both synthetic and real hyperspectral images.

Provides improved background removal and anomaly detection accuracy.

Abstract

In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, we train a GAN model to generate a synthetic background image which is close to the original background image as much as possible. By subtracting the synthetic image from the original one, we are able to remove the background from the hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli (RX) anomaly detector (AD) on the spectral difference image. In the experimental part, we compare our proposed method with the classical RX, Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic and real hyperspectral images. The detection results show that our proposed algorithm outperforms the other methods in the benchmark.