Mr. Renbo Luo Profile

Renbo Luo

at Ghent Univ

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Proceedings Article | 18 October 2016 Presentation + Paper

Spectral-spatial classification of hyperspectral images with semi-supervised graph learning

Renbo Luo, Wenzhi Liao, Hongyan Zhang, Youguo Pi, Wilfried Philips

Proceedings Volume 10004, 100040T (2016) https://doi.org/10.1117/12.2240652

KEYWORDS: Image classification, Hyperspectral imaging, Feature extraction, Matrices, Remote sensing, Image fusion, Principal component analysis, Statistical modeling, Lithium, Data fusion

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In this paper, we propose a novel semi-supervised graph leaning method to fuse spectral (of original hyperspectral (HS) image) and spatial (from morphological features) information for classification of HS image. In our proposed semi-supervised graph, samples are connected according to either label information (labeled samples) or their k-nearest spectral and spatial neighbors (unlabeled samples). Furthermore, we link the unlabeled sample with all labeled samples in one class which is the closest to this unlabeled sample in both spectral and spatial feature spaces. Thus, the connected samples have similar characteristics on both spectral and spatial domains, and have high possibilities to belong to the same class. By exploiting the fused semi-supervised graph, we then get transformation matrices to project high-dimensional HS image and morphological features to their lower dimensional subspaces. The final classification map is obtained by concentrating the lower-dimensional features together as an input of SVM classifier. Experimental results on a real hyperspectral data demonstrate the efficiency of our proposed semi-supervised fusion method. Compared to the methods using unsupervised fusion or supervised fusion, the proposed semi-supervised fusion method enables improved performances on classification. Moreover, the classification performances keep stable even when a small number of labeled training samples is available.

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