Multiscale Convolution Network with Region-Based Max Voting for Hyprrsprctral Imagrs Classificatton

Abstract

Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional handcrafted feature extraction methods, convolutional neural network (CNN) can automatically learn hierarchical features with discriminative information. However, two issues exist in applying CNN to HSI classification. One issue is how to represent the land covers at multiscale, the other is how to solve the “salt and pepper” noises caused by pixel-based CNN classification. To solve these issues, in this paper, a multiscale CNN is proposed to extract multiscale features for HSI classification, and then a region-based max voting scheme is applied to the classification map to solve the “salt and pepper” noises. Experiments on two classical data sets demonstrate that the proposed method is effective for HSI classification, especially for images with large scale changes.