5 January 2018 Dimensionality-varied deep convolutional neural network for spectral–spatial classification of hyperspectral data
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Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral–spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral–spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral–spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Haicheng Qu, Haicheng Qu, Xuejian Liang, Xuejian Liang, Shichao Liang, Shichao Liang, Wanjun Liu, Wanjun Liu, } "Dimensionality-varied deep convolutional neural network for spectral–spatial classification of hyperspectral data," Journal of Applied Remote Sensing 12(1), 016007 (5 January 2018). https://doi.org/10.1117/1.JRS.12.016007 . Submission: Received: 31 August 2017; Accepted: 12 December 2017
Received: 31 August 2017; Accepted: 12 December 2017; Published: 5 January 2018

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