Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

TLDR

Hyperspectral image classification is a hot topic in remote sensing, yet most existing methods extract features shallowly. This study introduces a deep learning approach for hyperspectral image classification. The authors propose a hybrid framework that uses a deep belief network to jointly learn spectral–spatial features via PCA, hierarchical RBM layers, and logistic regression for classification. Experiments show the DBN‑based classifier achieves competitive accuracy compared to state‑of‑the‑art methods, demonstrating the strong potential of deep learning for hyperspectral classification.

Abstract

Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral-spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification.