DS4L: Deep Semisupervised Shared Subspace Learning for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is essential in remote sensing image analysis. The classification methods based on deep learning have attracted more and more attention. However, classification accuracy is seriously affected by the quantity of labeled data and redundant information. Therefore, a deep semisupervised shared subspace learning (DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> L) model is developed to overcome these problems in this article. DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> L is composed of two parts. First, the basic feature extraction (BFE) network is constructed to preliminary extract high-dimensional-space features of multiscale data and fusion them to one shared subspace. Then, a deep shared subspace learning (DSSL) network is proposed to obtain a deeper and more representative low-dimensional subspace. Moreover, to obtain a more representative subspace and alleviate dependence on labeled samples, the regular, irregular constraint, and cross-entropy (CE) loss are integrated into the model. The regular constraint is adopted to reconstruct the multiscale patches to ensure the quality of the subspace in an unsupervised manner. The irregular constraint can well embed labeled and unlabeled samples into the procedure of subspace learning (SL). Then, the CE loss is used to extract more discriminative subspace using the limited labeled samples. Finally, we perform experiments on three widely used HSI datasets. Compared with the basic SL model, the DS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> L’s classification accuracy on the popular Salinas, Indian Pines, and PaviaU datasets are increased by 4.68%, 4.25%, and 1.57%, respectively.