TY - JOUR
T1 - Representation-based classifications with Markov random field model for hyperspectral urban data
AU - Xiong, Mingming
AU - Zhang, Fan
AU - Ran, Qiong
AU - Hu, Wei
AU - Li, Wei
PY - 2014/1
Y1 - 2014/1
N2 - 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%.
AB - 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%.
KW - Markov random field
KW - hyperspectral image classification
KW - representation-based classification
UR - http://www.scopus.com/inward/record.url?scp=84905748538&partnerID=8YFLogxK
U2 - 10.1117/1.JRS.8.085097
DO - 10.1117/1.JRS.8.085097
M3 - Article
AN - SCOPUS:84905748538
SN - 1931-3195
VL - 8
JO - Journal of Applied Remote Sensing
JF - Journal of Applied Remote Sensing
IS - 1
M1 - 085097
ER -