Anomaly Detection in Airborne Fourier Transform Thermal Infrared Spectrometer Images Based on Emissivity and a Segmented Low-Rank Prior

Xuhe Zhu,Lyuzhou Gao,Yanfei Zhong,Shaoyu Wang,Liqin Cao

doi:10.3390/rs13040754

Abstract

Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

Highlights

The data were collected from 2.5 km above the ground using the state-of-the-art thermal infrared imaging spectrometer called the Hyper-Cam-LW, which is made by Telops
In this paper, based on the characteristics of longwave infrared (LWIR) hyperspectral images, we have presented a new LWIR hyperspectral anomaly detection method named emissivity and a segmented low-rank prior (EaSLRP)
Method focuses on the separation of anomalies and background in LWIR hyperspectral images with low spectral contrast and signal-to-noise ratio (SNR). It makes full use of the original data and background information to separate the anomalies from the background

Summary

Introduction

Hyperspectral remote sensing target detection has developed rapidly in recent years and has become an effective means to remotely detect targets of interest [1,2,3]. Hyperspectral remote sensing imagery contains rich spectral information, and the high-dimensional features can effectively support the identification of different targets. Hyperspectral anomaly detection has broad prospects for civil and military applications, such as mineral exploration, border monitoring, search and rescue, military reconnaissance, etc. Many hyperspectral target detection methods have been proposed for use in the visible and Remote Sens.

Methods

Results

Discussion

Conclusion