Hyperspectral Image Classification Method Based on CNN Architecture Embedding With Hashing Semantic Feature

Abstract

Deep convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image (HSI) classification. In this paper, a CNN system embedded with an extracted hashing feature is proposed for HSI classification that utilizes the semantic information of the HSI. First, a series of hash functions are constructed to enhance the presentation of the locality and discriminability of classes. Then, the sparse binary hash codes calculated by the discriminative learning algorithm are combined into the original HSI. Next, we design a CNN framework with seven hidden layers to obtain the hierarchical feature maps with both spectral and spatial information for classification. A deconvolution layer aims to improve the robustness of the proposed CNN network and is used to enhance the expression of deep features. The proposed CNN classification architecture achieves powerful distinguishing ability from different classes. The extensive experiments on real hyperspectral images results demonstrate that the proposed CNN network can effectively improve the classification accuracy after the embedding of the extracted semantic features.