Learning a Deep Similarity Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is a challenging task due to subtle interclass difference and large intraclass variability, especially when the available training samples are scarce. To overcome this barrier, this article proposes a novel deep similarity network (DSN) for HSI classification, which not only ensures enough samples for training but also extracts more discriminative features. Unlike other classification methods, our essential idea is to approach the classification task by learning a new similarity measure of pixel pairs under a two-branch neural network. Specifically, a binary classification dataset with same-class and different-class pixel pairs is first constructed, which can significantly increase the number of training samples. Then, the DSN utilizes two subnetworks to extract deep features from the pixel pairs, and computes the similarity between the extracted deep features by a fusion subnetwork. Finally, the output of the DSN is used to measure the similarity to each class and the similarity determines the class label. To make full use of the spatial information, the extended multiattribute profile is incorporated to the DSN. Moreover, a joint loss function is proposed to enhance the discrimination and alleviate the challenge caused by the spatial variability of spectral signatures. Experiments on real HSI datasets verify the superiority of the DSN over several state-of-the-art methods in HSI classification. For instance, the overall accuracy of the DSN on Houston2013 dataset is 89.07%, which achieves a marked improvement of at least 4.2% over all compared methods like convolutional neural network, deep learning with attribute profiles and so on.

Automated summary

This article proposes a novel deep similarity network (DSN) for hyperspectral image (HSI) classification, which improves classification accuracy by learning a new similarity measure of pixel pairs. By constructing a two-classification dataset, extracting features using two subnetworks, and computing similarity using a fusion subnetwork, the DSN demonstrates superiority in HSI classification.