Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

Abstract

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.

Matrix Autoregressive Model for Hyperspectral Anomaly Detection

“…It should be noted that both the coefficient 1 2 and square operation of the Frobenius norm are utilized for the convenience of optimization, while making no effect on the optimal variables of the functions.…”

“…Hyperspectral imagery captures both the spatial and the spectral information of the scene simultaneously, achieving a three-dimensional(3D) image cube [1]. The 3D hyperspectral images (HSIs) are characterized by their rich spectral information, which can be utilized to identify the materials by their unique reflective spectra [2].…”

Hyperspectral unmixing-based anomaly detection

“…Conversely, the pixel's spectral signature can result from a linear or non-linear combination of more than one endmember, and in this case, it is called a mixed pixel. 2 Spectral unmixing is a process that decomposes the measured spectra into a collection of endmembers while indicating the proportion of each endmember in the pixel. 3 The spectral unmixing process starts with identifying/extracting the number of representative endmembers in the hyperspectral image; then an endmember estimating algorithm is used to identify those unique endmembers.…”

Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection

“…The spectral dimension of the HSI usually ranges from the visible to the near-infrared wavelength by a step of less than 10 nm. The rich and detailed spectral information is the key for accurate identification of the subtle difference between different objects [2], [3].…”