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Spectral–spatial classification of hyperspectral image using semisupervised tritraining and extended attribute profiles

Abstract. Recent studies have shown that spatial information is necessary for hyperspectral image classification, but how to appropriately integrate the distinct spectral and spatial information is still an open issue. Meanwhile, the illposed classification problem due to the limited labeled samples also results in deterioration of recognition performance. An integration of the labeled and unlabeled samples for class discriminant information through semisupervised learning is one of the promising solutions. An integration method of combining different kinds of spectral–spatial features through a semisupervised tritraining learning scheme is proposed. In the method, the dimensionality reduction process is first conducted for a hyperspectral image to reduce the data amount. Subsequently, an extended attribute profile (EAP) is used for texture extraction and spectral–spatial feature stacking. Three different attributions including area, moment of inertia, and diagonal of the box bounding the region are calculated based on the reduced image. Finally, the three kinds of concatenated features are used as inputs of tritraining with three supervised classifiers. These classifiers are trained and refined by the labeled and unlabeled samples in the tritraining process, and an improvement in the final classification accuracy is achieved. In tritraining, to address the problem of the invalid estimation of the classifier error due to the limited labeled samples, a technique based on the posterior probabilities of samples is adopted to estimate the error. Experiments on three benchmark hyperspectral datasets indicate that the proposed method can effectively integrate the information from the spectra, texture, labeled, and unlabeled samples for land cover classification.