Improvement of Anomoly Detection Algorithms in Hyperspectral Images using Discrete Wavelet Transform

TL;DR

This paper introduces a novel hyperspectral anomaly detection method using Discrete Wavelet Transform to enhance detection accuracy and computational speed, demonstrated through experiments on AVIRIS datasets.

Contribution

The paper proposes a new approach applying DWT to improve the efficiency and accuracy of existing anomaly detection algorithms in hyperspectral images.

Findings

Significant reduction in runtime of AD algorithms.

Improved detection accuracy due to DWT-based feature extraction.

Effective enhancement demonstrated on AVIRIS hyperspectral datasets.

Abstract

Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of "Wavelet transform" matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT's power in extracting approximation coefficients of signal, which contain the main behaviour of signal, and abandon the redundant information in hyperspectral image data.