9 December 2015 Sparse representation and smooth filtering for hyperspectral image classification
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Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 98083P (2015) https://doi.org/10.1117/12.2205325
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China
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.
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Mengmeng Zhang, Qiong Ran, Wei Li, Kui Liu, "Sparse representation and smooth filtering for hyperspectral image classification", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98083P (9 December 2015); doi: 10.1117/12.2205325; https://doi.org/10.1117/12.2205325
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