Non-overlapping classification of hyperspectral imagery based on set-to-sets distance

Abstract

Spectral–spatial classification methods can improve the classification accuracy of hyperspectral imagery (HSI) dramatically. However, this improvement is caused in part by the overlap between training set and test set. In this paper, a novel non-overlapping spectral-spatial classification framework based on set-to-sets distance is proposed. First, each class is sampled using a controlled random sampling method, which is regarded as a training set. The image is segmented into many superpixels and each superpixel is taken as a test set. In order to ensure that the test set and the training set are not overlapped with each other, the training pixels contained in each test superpixel are deleted. Then, the training set of each class is compressed into a more compact set to reduce the computational complexity and kernel trick is used to make samples be approximately linearly separable. Finally, each test set is modeled as a convex hull, this hull is represented collaboratively with all training sets. With the resolved representation coefficients, the distance between the test set and each training set can be calculated for classification. Experimental results based on three real HSI data sets demonstrate the superiority of the proposed method to state-of-the-art algorithms under the non-overlapping sampling strategy.