Classification based on deep convolutional neural networks with hyperspectral image

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very rich spatial and spectral information. However, the high-dimensional data may lead to the curse of dimensionality phenomenon when it is used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we developed a deep learning classification framework based on the spectral and spatial information of hyperspectral image. Firstly, the deep learning features in different layers could be extracted automatically. Secondly, based on the learned deep learning features, we could obtain the classification of hyperspectral image with logistic regression (LR) classifier. Finally, we compared our approach with other methods including quadratic discriminant analysis with the multilevel logistic spatial prior (QDAMLL), logistic discriminant analysis with the multilevel logistic spatial prior (logDAMLL), linear discriminant analysis with the multilevel logistic spatial prior (LDAMLL), subspace multiclass logistic regression with the multilevel logistic spatial prior (MLRsub MLL), support vector machine on extended morphological profiles (SVM/EMP), support vector machine on expectation maximization and post-regularization (SVM-EM-PR). The experimental results showed that our method obtained the optimum accuracy, which was better than the other six approaches. And the OA was up to 99.39%. Therefore, the deep convolutional neural networks (DCNNs) is a robust method for land use classification with hyperspectral image.