Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
نویسندگان
چکیده
منابع مشابه
Hyperspectral Unmixing: Geometrical, Statistical, and Sparse Regression-Based Approaches
Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquire...
متن کاملHyperspectral Unmixing with Robust Collaborative Sparse Regression
Chang Li 1, Yong Ma 2,∗, Xiaoguang Mei 2, Chengyin Liu 1 and Jiayi Ma 2 1 School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China; [email protected] (C.L.); [email protected] (C.L.) 2 Electronic Information School, Wuhan University, Wuhan 430072, China; [email protected] (X.M.); [email protected] (J.M.) * Corresponden...
متن کاملA Sparse Regression Approach to Hyperspectral Unmixing
Spectral unmixing is an important problem in hyperspectral data exploitation. It amounts at characterizing the mixed spectral signatures collected by an imaging instrument in the form of a combination of pure spectral constituents (endmembers), weighted by their correspondent abundance fractions. Linear spectral unmixing is a popular technique in the literature which assumes linear interactions...
متن کاملSparse Hyperspectral Unmixing
Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. A semi-supervised approach to deal with the linear spectral unmixing problem consists in assuming that the observed spectral vectors are linear combinations of a small num...
متن کاملStructured Sparse Method for Hyperspectral Unmixing
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2012
ISSN: 1939-1404,2151-1535
DOI: 10.1109/jstars.2012.2194696