DocumentCode :
70054
Title :
Active Landmark Sampling for Manifold Learning Based Spectral Unmixing
Author :
Junhwa Chi ; Crawford, Melba M.
Author_Institution :
Lab. for Applic. of Remote Sensing, Purdue Univ., West Lafayette, IN, USA
Volume :
11
Issue :
11
fYear :
2014
fDate :
Nov. 2014
Firstpage :
1881
Lastpage :
1885
Abstract :
Nonlinear manifold learning based spectral unmixing provides an alternative to direct nonlinear unmixing methods for accommodating nonlinearities inherent in hyperspectral data. Although manifolds can effectively capture nonlinear features in the dimensionality reduction stage of unmixing, the computational overhead is excessive for large remotely sensed data sets. Manifold approximation using a set of distinguishing points is commonly utilized to mitigate the computational burden, but selection of these landmark points is important for adequately representing the topology of the manifold. This study proposes an active landmark sampling framework for manifold learning based spectral unmixing using a small initial landmark set and a computationally efficient backbone-based strategy for constructing the manifold. The active landmark sampling strategy selects the best additional landmarks to develop a more representative manifold and to increase unmixing accuracy.
Keywords :
geophysical signal processing; geophysical techniques; learning (artificial intelligence); remote sensing; signal sampling; spectral analysis; active landmark sampling framework; active landmark sampling strategy; backbone-based strategy; computational overhead; dimensionality reduction; direct nonlinear unmixing method; hyperspectral data; landmark point selection; large remotely sensed data set; manifold approximation; manifold topology representation; nonlinear feature capture; nonlinear manifold learning based spectral unmixing; Approximation methods; Geometry; Hyperspectral imaging; Manifolds; Principal component analysis; Active learning; hyperspectral remote sensing; landmark selection; locally linear embedding (LLE); manifold learning; spectral mixture analysis; spectral unmixing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2014.2312619
Filename :
6784516
Link To Document :
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