Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing
نویسندگان
چکیده
منابع مشابه
Linear and Nonlinear Unmixing in Hyperspectral Imaging
N. Dobigeon*, Y. Altmann, N. Brun and S. Moussaoui University of Toulouse, IRIT/INP-ENSEEIHT, 31071 Toulouse Cedex 7, France School of Engineering and Physical Sciences, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS, United Kingdom Laboratoire de Physique des Solides, CNRS, Univ. Paris-Sud, Univ. Paris-Saclay, 91405 Orsay Cedex, France Ecole Centrale de Nantes, IRCCyN, UMR CNRS 6597, N...
متن کاملHyperspectral 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 کاملLinear Mixture Models, Full and Partial Unmixing in Multi- and Hyperspectral Image Data
As a supplement or an alternative to classification of hyperspectral image data the linear mixture model is considered in order to obtain estimates of abundance of each class or endmember in pixels with mixed membership. Full unmixing and the partial unmixing methods orthogonal subspace projection (OSP), constrained energy minimization (CEM) and an eigenvalue formulation alternative are dealt w...
متن کاملLinear Unmixing of Hyperspectral Signals via Wavelet
A pixel in remotely sensed hyperspectral imagery is typically a mixture of multiple electromagnetic radiances from various ground cover materials. Spectral unmixing is a quantitative analysis procedure used to recognize constituent ground cover materials (or endmembers) and obtain their mixing proportions (or abundances) from a mixed pixel. The abundances are typically estimated using the least...
متن کاملHyperspectral Image Unmixing: Accounting For Wavelength Dependence∗
We introduce a method for hyperspectral unmixing that incorporates wavelength dependence in addition to spatial dependence. Spatial dependence is incorporated into the model using class labels on the pixels that is assigned using spectral clustering. Wavelength dependence is introduced by correlating the errors in the unmixing regression models. We propose a non-standard alternating direction m...
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 Signal Processing Letters
سال: 2017
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2017.2747478