Representation-based classifications with Markov random field model for hyperspectral urban data

Mingming Xiong; Fan Zhang; Qiong Ran; Wei Hu; Wei Li

doi:10.1117/1.JRS.8.085097

28 July 2014 Representation-based classifications with Markov random field model for hyperspectral urban data

Mingming Xiong, Fan Zhang, Qiong Ran, Wei Hu, Wei Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 085097 (July 2014). https://doi.org/10.1117/1.JRS.8.085097

Abstract

Recently, representation-based classifications have gained increasing interest in hyperspectral imagery, such as the newly proposed sparse-representation classification and nearest-regularized subspace (NRS). These classifiers provide excellent performance that is comparable to or even better than the classic support vector machine. However, all these representation-based methods were originally designed to be pixel-wise classifiers which only consider the spectral signature while ignoring the spatial-contextual information. A Markov random field (MRF), providing a basis for modeling contextual constraints, has currently been successfully applied for hyperspectral image analysis. We mainly investigate the benefits of combining these representation-based classifications with an MRF model in order to acquire better classification results. Two real hyperspectral images are used to validate the proposed classification scheme. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art approaches. For example, NRS-MRF performed with an accuracy of 94.92% for the Reflective Optics System Imaging Spectrometer data with 60 training samples per class, while the original NRS obtained an accuracy of 81.95%, an improvement of approximately 13%.

Citation Download Citation

Mingming Xiong, Fan Zhang, Qiong Ran, Wei Hu, and Wei Li "Representation-based classifications with Markov random field model for hyperspectral urban data," Journal of Applied Remote Sensing 8(1), 085097 (28 July 2014). https://doi.org/10.1117/1.JRS.8.085097

Published: 28 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