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.
REF proposed a series of regularized deep feature extraction methods using several convolution and pooling layers, which achieved the state-of-the-art performance.
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