Spatial/spectral endmember extraction by multidimensional morphological operations

Abstract

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.

HyperMix: A new tool for quantitative evaluation of end member identification and spectral unmixing techniques

“…Endmember selection. For this purpose, the tool includes algorithms using spectral information: N-FINDR [8], orthogonal subspace projection (OSP) [9], vertex component analysis (VCA) [10] and also algorithms using both spatial and spectral information: automated morphological endmember extraction (AMEE) [11], spatial-spectral endmember extraction (SSEE) [12], and spatial pre-processing (SPP) [13].…”

Spectral unmixing via minimum volume simplices: Application to near infrared spectra of counterfeit tablets

“…Many algorithms have been designed to exploit this scenario, aiming at finding the spectra of the vertices of the simplex: the pixel purity index [6], N-FINDR [7], vertex component analysis [4], simplex growing algorithm [8] and automated morphological endmember extraction [9].…”

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

“…Spectral mixture analysis (SMA) intends to estimate fractional abundance of pure ground objects within a mixed pixel [8]. Such analysis can be fulfilled by extracting endmembers [9,10] and estimating their fractional abundance [11] separately, or treating these two problems simultaneously as a blind signal decomposition problem, for which non-negative matrix factorization (NMF) [12,13], convex optimization [14], and Neural Network (NN) based techniques [15] are widely used. Sub-pixel level target detection has also been proposed to detect objects of interest within a pixel [16,17].…”