Volume 19, No. 2, 2022
Spectral Unmixing In HSI: A Case Study
Kriti , Urvashi Garg
Abstract
In HSI, the unmixing of images plays an imperative role since the initial years. The collection of spectral signatures from the existing environments is perpetually its fusion of several constituents originated in the spatial range. HSI obtains a three-dimensional dataset called hypercube, having one spectral and two spatial dimensions. The exploration of HSI is based on the spectral decay of the pixels through the spectral unmixing method, having applications in detection of the target, unsupervised segmentation of the image, etc. The perilous part is to define the endmembers used as the references for the process of unmixing. Hence, inclusive details of the unmixing method are required as its application is extensively growing. The unmixing methods are summarized in three categories: extraction of endmember, selection of endmember combinations, and abundance estimation. The paper consequently provides an outline of HSI unmixing and its applications. The primary objective is to provide a historical outline of the popular methods of unmixing and to discuss certain popular techniques in detail. In HSI unmixing, LMM is the dominant archetypal besides it is a foundation of a huge diversity of unmixing methods in HSI, hence a prominent part of the review embraces it. Furthermore, in the HSI unmixing, nonlinearity is a critical factor in real-world situations. While numerous models for nonlinear unmixing are projected, in recent times there occurred an explosion of nonlinear models for unmixing. Henceforth, we deliver an outline of some recent expansions in the nonlinear unmixing literature. The objective of the paper is threefold: an overview for new researchers and for those already functioning and searching for literature in this arena. The quantitative measure of certain existing methods helps to analyze current progress and to anticipate imminent growth. Lastly, the review is structured according to the elementary computational method of unmixing: geometrical, statistical, and heuristic approaches with a small segment of statistical versus geometrical methods.
Pages: 7068-7092
Keywords: hyperspectral images (HSI), hyperspectral unmixing (HU), neural network (NN), reconstruction error (RE), endmember determination (ED), and dimension reduction (DR).