Hyperspectral Unmixing with Bandwise Generalized Bilinear Model

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Hyperspectral image unmixing via bilinear generalized approximate message passing

In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In parti...

متن کامل

Unmixing Hyperspectral Data

In hyperspectral imagery one pixel typically consists of a mixture of the re ectance spectra of several materials, where the mixture coe cients correspond to the abundances of the constituting materials. We assume linear combinations of re ectance spectra with some additive normal sensor noise and derive a probabilistic MAP framework for analyzing hyperspectral data. As the material reectance c...

متن کامل

Hyperspectral Eels Image Unmixing

Y. Altmann, N. Brun , N. Dobigeon , K. March, S. Moussaoui, O. Schneegans 1School of Engineering and Physical Sciences, Heriot-Watt University Earl Mountbatten Building, Riccarton, EH14 4AS, Edinburgh, U.K. Laboratoire de Physique des Solides, CNRS UMR 8502, Univ. Paris-Sud, Univ. Paris-Saclay Bât. 510, 91405 Orsay Cedex, France University of Toulouse, IRIT/INP-ENSEEIHT/TéSA 2 rue Charles Camic...

متن کامل

Sparse 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 کامل

Blind Hyperspectral Unmixing

This paper introduces a new hyperspectral unmixing method called Dependent Component Analysis (DECA). This method decomposes a hyperspectral image into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA models the abundance fractions as mixtures of Dirichlet densities, thus ...

متن کامل

ذخیره 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: Remote Sensing

سال: 2018

ISSN: 2072-4292

DOI: 10.3390/rs10101600