A probabilistic framework for weighted combination of multiple-feature classifications of hyperspectral images

Spatial information such as texture and shape features as well as spatial contextual information play a key role in representation and analysis of hyperspectral images. Spatial information improves the classification accuracy and addresses the common problem of pixel-wise classification methods, i.e. limited training samples. In this article, a new combination of spectral, texture and shape features, as well as, contextual information in the probabilistic framework is proposed. The texture features are extracted utilizing Gabor filters and the shape features are represented by morphological profiles. The spectral, texture and shape features are separately fed into a probabilistic support vector machine classifier to estimate the per-pixel probability. These probabilities are combined together to calculate the total probability on which three weights determine the efficacy of each one. Finally, the classification result obtained in the previous step is refined by majority voting within the shape adaptive neighbourhood of each pixel. Instead of the simple majority vote we applied the majority vote in the probabilistic framework on which the reliability of the labels in the region is also considered. Experiments on three hyperspectral images: Indian Pines, Pavia University, and Salinas demonstrate the efficiency of the proposed method for the classification of hyperspectral images, especially with limited training samples. Moreover, after comparing with some recent spectral-spatial classification methods, the performance of the proposed method is demonstrated.