Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit

Jinyi Zou; Wei Li; Xin Huang; Qian Du

doi:10.1117/1.JRS.8.085099

18 July 2014 Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit

Jinyi Zou, Wei Li, Xin Huang, Qian Du

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 085099 (July 2014). https://doi.org/10.1117/1.JRS.8.085099

Abstract

Simultaneous orthogonal matching pursuit (SOMP) has been recently developed for hyperspectral image classification. It utilizes a joint sparsity model with the assumption that each pixel can be represented by a linear combination of labeled samples. We present an approach to improve the performance of SOMP based on a priori segmentation map. According to the map, we first build a local region where within-segment pixels are preserved while between-segment pixels are excluded. Hyperspectral pixels in the preserved region around the test pixel are then simultaneously represented by a linear combination of training samples, whose weights are recovered by solving a sparsity-constrained optimization problem. Finally, the label of the test pixel is determined to be the class that yields the minimal total residuals between the test samples and the approximations. Experimental results demonstrate that the proposed adaptive SOMP (ASOMP) is superior to some existing classifiers, such as the original SOMP and the recently proposed weighted-SOMP (WSOMP). For example, the ASOMP performed with an accuracy of 95.53% for the ROSIS University of Pavia data with 120 training samples per class, while SOMP obtained an accuracy of 87.61%, an improvement of approximately 8%.

Citation Download Citation

Jinyi Zou, Wei Li, Xin Huang, and Qian Du "Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit," Journal of Applied Remote Sensing 8(1), 085099 (18 July 2014). https://doi.org/10.1117/1.JRS.8.085099

Published: 18 July 2014

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