The sequential maximum angle convex cone (SMACC) endmember model
نویسندگان
چکیده
A new endmember extraction method has been developed that is based on a convex cone model for representing vector data. The endmembers are selected directly from the data set. The algorithm for finding the endmembers is sequential: the convex cone model starts with a single endmember and increases incrementally in dimension. Abundance maps are simultaneously generated and updated at each step. A new endmember is identified based on the angle it makes with the existing cone. The data vector making the maximum angle with the existing cone is chosen as the next endmember to add to enlarge the endmember set. The algorithm updates the abundances of previous endmembers and ensures that the abundances of previous and current endmembers remain positive or zero. The algorithm terminates when all of the data vectors are within the convex cone, to some tolerance. The method offers advantages for hyperspectral data sets where high correlation among channels and pixels can impair un-mixing by standard techniques. The method can also be applied as a band-selection tool, finding end-images that are unique and forming a convex cone for modeling the remaining hyperspectral channels. The method is described and applied to hyperspectral data sets.
منابع مشابه
Geometrical Endmember Extraction and Linear Spectral Unmixing of Multispectral Image
Accurate mapping is prepared using Linear unmixing of satellite images. Endmember extraction contributes the unmixing accuracy. In this paper, Endmembers are extracted using different Geometrical algorithms like Pixel Purity Index (PPI), Nearest Finder (N-FINDR) and Sequential Maximum Angle Convex Cone (SMACC) algorithms. Extracted Endmembers are given as input for unmixing and it is attempted ...
متن کاملConstrained Basis Set Expansions for Target Subspaces in Hyperspectral Detection and Identification
Subspace methods for hyperspectral imagery enable detection and identification of targets under unknown environmental conditions (i.e., atmospheric, illumination, surface temperature, etc.) by specifying a subspace of possible target spectral signatures (and, optionally, a background subspace) and identifying closely fitting spectra in the image. The subspaces, defined from a set of exemplar sp...
متن کاملThe Extension of Endmember Extraction to Multispectral Scenes
A multiple simplex endmember extraction method has been developed. Unlike convex methods that rely on a single simplex, the number of endmembers is not restricted by the number of linearly independent spectral channels. The endmembers are identified as the extreme points in the data set. The algorithm for finding the endmembers can simultaneously find endmember abundance maps. Multispectral and...
متن کاملBlind spatial unmixing of multispectral images: New methods combining sparse component analysis, clustering and non-negativity constraints
Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of th...
متن کاملAn Investigation of Likelihoods and Priors for Bayesian Endmember Estimation
A Gibbs sampler for piece-wise convex hyperspectral unmixing and endmember detection is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data is often nonconvex. Furthermore, in standard unmixing methods, endmembers are generally represented as a single point in the high dimension...
متن کامل