@inproceedings{c7dbf174d44e47c2a3e3dbf648972ff6,
title = "Sparse representation and smooth filtering for hyperspectral image classification",
abstract = "Sparse representation-based classification (SRC) has gained great interest recently. A pixel to be classified is sparsely approximately by labeled samples, and it is assigned to the class whose labeled samples provide the smallest representation error. In this paper, we extend SRC by exploiting the benefits of using a smoothing filter based on sparse gradient minimization. The smoothing filter is expected to provide less intra class variability and more spatial regularity, which eliminating the inherent variations within a small neighborhood. Classification performance on two real hyperspectral datasets demonstrates that our proposed method has improved classification accuracy and the resulting accuracies are persistently higher at all small training sample size situations compared to some traditional classifiers.",
keywords = "Hyperspectral imagery, gradient minimization filter, sparse representation-based classification",
author = "Mengmeng Zhang and Qiong Ran and Wei Li and Kui Liu",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 2015 International Conference on Intelligent Earth Observing and Applications, IEOAs 2015 ; Conference date: 23-10-2015 Through 24-10-2015",
year = "2015",
doi = "10.1117/12.2205325",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Chuanli Kang and Guoqing Zhou",
booktitle = "International Conference on Intelligent Earth Observing and Applications 2015",
address = "United States",
}