Linear Regression Fisher Discrimination Dictionary Learning for Hyperspectral Image Classification

Chen, Liang; Yang, Ming; Deng, Cheng; Yin, Hujun

doi:10.1007/978-3-319-10840-7_31

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2015

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“…Nowadays, a considerable amount of research has been done on hyperspectral image classification using machine learning algorithms during the past decade. Chen [1] propose a novel dictionary learning method for hyperspectral image classification. Its proposed method, linear regression fisher discrimination dictionary learning, obtains a more discriminative dictionary and a classifier by incorporating linear regression term and the fisher discrimination into the objective function during training, which improves the performances of classification.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification based on Co-training

Zheng¹,

Peng²

2015

IJHIT

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The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, "Curse of dimensionality" and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classification. Firstly, two views of samples are generated through two kinds of dimensionality reduction methods. After that, the co-training process is viewed as combinative label propagation over two independent views. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%