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ISPRS Int. J. Geo-Inf. 2018, 7(7), 284; https://doi.org/10.3390/ijgi7070284

Semi-Supervised Classification for Hyperspectral Images Based on Multiple Classifiers and Relaxation Strategy

1
College of Urban and Environment, Liaoning Normal University, Dalian 116029, China
2
College of Computer Science, Liaoning Normal University, Dalian 116081, China
*
Author to whom correspondence should be addressed.
Received: 11 May 2018 / Revised: 11 July 2018 / Accepted: 19 July 2018 / Published: 23 July 2018
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Abstract

Hyperspectral image (HSI) classification is a fundamental and challenging problem in remote sensing and its various applications. However, it is difficult to perfectly classify remotely sensed hyperspectral data by directly using classification techniques developed in pattern recognition. This is partially owing to a multitude of noise points and the limited training samples. Based on multinomial logistic regression (MLR), the local mean-based pseudo nearest neighbor (LMPNN) rule, and the discontinuity preserving relaxation (DPR) method, in this paper, a semi-supervised method for HSI classification is proposed. In pre-processing and post-processing, the DPR strategy is adopted to denoise the original hyperspectral data and improve the classification accuracy, respectively. The application of two classifiers, MLR and LMPNN, can automatically acquire more labeled samples in terms of a few labeled instances per class. This is termed the pre-classification procedure. The final classification result of the HSI is obtained by employing the MLRsub approach. The effectiveness of the proposal is experimentally evaluated by two real hyperspectral datasets, which are widely used to test the performance of the HSI classification algorithm. The comparison results using several competing methods confirm that the proposed method is effective, even for limited training samples. View Full-Text
Keywords: hyperspectral image; semi-supervised classification; multinomial logistic regression; local mean-based pseudo nearest neighbor; discontinuity preserving relaxation hyperspectral image; semi-supervised classification; multinomial logistic regression; local mean-based pseudo nearest neighbor; discontinuity preserving relaxation
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Xie, F.; Hu, D.; Li, F.; Yang, J.; Liu, D. Semi-Supervised Classification for Hyperspectral Images Based on Multiple Classifiers and Relaxation Strategy. ISPRS Int. J. Geo-Inf. 2018, 7, 284.

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