Classification Merged with Clustering and Context for Hyperspectral Imagery

The traditional pixel-wised classification methods for hyperspectral image (HIS) only consider spectral information while ignoring the spatial information, resulting in a big limit of classification performance. Clustering which could assemble pixels similar in spectral features into spatial adjacent clusters, thus effectively express similarity and spatial correlation of adjacent pixels. In order to take full advantages of spatial correlation, this paper explore a spectral-spatial classification method for HSI merged with clustering and context. Firstly, under condition of different feature extraction(MNF, ICA and PCA), different clustering methods(k-means, ISODATA and FCM) are used in hidden markov random field to obtain optimized segmentation map containing context features; secondly, the regions in the segmentation map are labeled by using a four-connected neighborhood labeling method to generate image objects, and a majority voting method is used to classify the objects based on the initial classification map derived from support vector machine (SVM) optimized by particle swarm optimization (PSO). Finally, a Chamfer neighborhood filtering technique is used to regularize the classification map, which partially reduces the noise. This method utilizing spatial information from clustering and introducing context features from HMRF takes advantage of supervised classification and unsupervised classification to gain noise reduction, high-accuracy and high homogeneity, which makes up for the inadequacy of the classification based only on spectral information. Experiment on ROSIS data set and AVIRIS data set respectively illustrate that the method can obtain better performance in terms of classification. The overall accuracy of ROSIS data set reaches to 98.53%, 5.01% higher than that obtained by SVM. Meanwhile the overall accuracy of AVIRIS data set climbs to 91.97%, 7.01% higher than SVM result. We also find that different feature extraction and different clustering will influence the spectral-spatial method using HMRF with edge-protection.