Abstract-Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection.
Chen et al. REF proposed a regularized deep feature extraction method is presented fro hyperspectral image classification using a convolutional neural network, which with sparse constraints provide competitive results to state-of-theart methods.
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing
Computer Science