In this paper, a novel framework for an accurate spectral-spatial classification of hyperspectral images is proposed to address nonlinear classification problems. The algorithm is based on the spectral angle mapper (SAM), which is achieved by introducing the multi-center model and Markov random fields (MRF) into a probabilistic decision framework to obtain an accurate classification. Experimental comparisons between several traditional classification methods and the proposed MSAM-MRF algorithm have demonstrated that the performance of the proposed MSAM-MRF algorithm outperforms the traditional classification algorithms.

Hyperspectral images collected by a remote sensing hyperspectral imaging instrument have many mixed pixels, due to the limited resolution of image sensors and the complex diversity of nature. End-member extraction is the process that determines the end-members in mixed pixels. The results of traditional methods are inaccurate, due to the spatial complexity and noise of actual hyperspectral image data. This study presents segmented vertex component analysis (SVCA), wherein the relative complexities of hyperspectral images are segmented into a number of relatively simple spatial subsets to reduce the effect of uncorrelated pixels. The end-members are extracted by finding the vertices of the simplex that minimally encloses the hyperspectral image data in each spatial subset, and the inversion abundance is used to identify each major end-member in each subset. Experimental results demonstrate that the proposed method can effectively implement end-member extraction with high accuracy.

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The limited resolution of image sensors and the complex diversity of nature, cause mixed pixel problems in hyperspectral technology. Such problems are common, and increase the complexity of hyperspectral image processing. Hyperspectral unmixing is crucial for hyperspectral image classification and recognition. In unmixing, the image signatures are represented as a linear combination of the basic materials. Unmixing is the process of decomposing a mixed pixel into constituent materials, and calculating the corresponding fractional abundance. If pure materials (end members) are present in an image, unmixing can be divided into two steps, namely, end member extraction and abundance decomposition. On the other hand, if there is no pure material, researchers have devised and investigated unsupervised and semi-supervised spectral unmixing technology. This article presents an overview of the state-of-the-art methods of hyperspectral unmixing and their extensions.