FrHPI: A Discriminative Patch-Image Model for Hyperspectral Anomaly Detection

Ganghui Fan,Hao Li,Zhen Kang,Xiaojie Guo,Shan Zeng

doi:10.1155/2021/6854954

Abstract

Anomaly detection is now a significantly important part of hyperspectral image analysis to detect targets in an unsupervised manner. Traditional hyperspectral anomaly detectors fail to consider spatial information, which is vital in hyperspectral anomaly detection. Moreover, they usually take the raw data without feature extraction as input, limiting the detection performance. We propose a new anomaly detector based on the fractional Fourier transform (FrFT) and a modified patch-image model called the hyperspectral patch-image (HPI) model to tackle these two problems. By combining them, the proposed anomaly detector is named fractional hyperspectral patch-image (FrHPI) detector. Under the assumption that the target patch-image is a sparse matrix while the background patch-image is a low-rank matrix, we first formulate a matrix by sliding a rectangle window on the first three principal components (PCs) of HSI. The matrix can be decomposed into three parts representing the background, targets, and noise with the well-known low-rank and sparse matrix decomposition (LRaSMD). Then, distinctive features are extracted via FrFT, a transformation which is desirable for noise removal. Background atoms are selected to construct the covariance matrix. Finally, anomalies are picked up with Mahalanobis distance. Extensive experiments are conducted to verify the proposed FrHPI detector’s superiority in hyperspectral anomaly detection compared with other state-of-the-art detectors.

Highlights

Hyperspectral imagery (HSI), with hundreds of narrow bands, can provide more abundant spectral information than other remote sensing approaches, such as infrared images and multispectral images [1]
As an essential part in hyperspectral image analysis, anomaly detection aims at identifying targets in an unsupervised manner, which possesses the following characteristics: (1) the spectral curves of anomalies are different from those of the surrounding background, and they only occupy a tiny part of the entire image; (2) no spectral information about background or targets are known in advance
We propose a fractional Fourier transform (FrFT) and hyperspectral patchimage (HPI) model-based anomaly detector named fractional hyperspectral patch-image (FrHPI) detector

Summary

Introduction

Hyperspectral imagery (HSI), with hundreds of narrow bands, can provide more abundant spectral information than other remote sensing approaches, such as infrared images and multispectral images [1]. Due to the corruption of noise and low spatial resolution, the spectral curves may share similar patterns between background and anomalies, whereas the algorithms mentioned above reconstruct the background merely with the raw data, which limits the final detection performance. The representation-based algorithms usually perform in pixel-level without consideration of spatial information To address these problems, we propose a fractional Fourier transform (FrFT) and hyperspectral patchimage (HPI) model-based anomaly detector named fractional hyperspectral patch-image (FrHPI) detector. (1) e proposed HPI takes full advantage of the nonlocal self-correlation property of the background and the sparsity of anomalies, and the background is separated by solving the optimization problem of recovering low-rank and sparse matrices with the well-known LRaSMD, which serves as an indicator for background covariance matrix formulation.

Procedure and Methods

Case Study

Findings

Conclusions