Low-complexity multiple collaborative representations for hyperspectral image classification

Yan Xu; Qian Du; Wei Li; Nicolas H. Younan

doi:10.1117/12.2278841

5 October 2017 Low-complexity multiple collaborative representations for hyperspectral image classification

Yan Xu, Qian Du, Wei Li, Nicolas H. Younan

Proceedings Volume 10430, High-Performance Computing in Geoscience and Remote Sensing VII; 1043003 (2017) https://doi.org/10.1117/12.2278841
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

Collaborative representation has been a popular classifier for hyperspectral image classification because it can offer excellent classification accuracy with a closed-form solution. Collaborative representation can be implemented using a dictionary with training samples of all-classes, or using class-specific sub-dictionaries. In either case, a testing pixel is assigned to the class whose training samples offer the minimum representation residual. The Collaborative Representation Optimized Classifier with Tikhonov regularization (CROCT) was developed to combine these two types of collaborative representations to achieve the balance for optimized performance. The class-specific collaborative representation involves inverse operation of matrices constructed from class-specific samples, and the all-class version requires inversion operation of the matrix constructed from all samples. In this paper, we propose a low-complexity CROCT to avoid redundant operations in all-class and class-specific collaborative representations. It can further reduce the computational cost of CROCT while maintaining its excellent classification performance.

Conference Presentation

Citation Download Citation

Yan Xu, Qian Du, Wei Li, and Nicolas H. Younan "Low-complexity multiple collaborative representations for hyperspectral image classification", Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 1043003 (5 October 2017); https://doi.org/10.1117/12.2278841

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available