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Spectral Regression Discriminant Analysis for Hyperspectral Image Classification : Volume Xxxix-b3, Issue 1 (01/08/2012)

By Pan, Y.

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Book Id: WPLBN0004016550
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Spectral Regression Discriminant Analysis for Hyperspectral Image Classification : Volume Xxxix-b3, Issue 1 (01/08/2012)  
Author: Pan, Y.
Volume: Vol. XXXIX-B3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus Publications
Historic
Publication Date:
2012
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Huang, H., Wu, J., Pan, Y., & Liu, J. (2012). Spectral Regression Discriminant Analysis for Hyperspectral Image Classification : Volume Xxxix-b3, Issue 1 (01/08/2012). Retrieved from http://ebooklibrary.org/


Description
Description: Key Lab. on Opto-electronic Technique and systems, Ministry of Education, Chongqing University, Chongqing, P.R. China, 400044. Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.

Summary
SPECTRAL REGRESSION DISCRIMINANT ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION


 

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