4 January 2018 Spectral–spatial classification of hyperspectral image using three-dimensional convolution network
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Abstract
Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Bing Liu, Bing Liu, Xuchu Yu, Xuchu Yu, Pengqiang Zhang, Pengqiang Zhang, Xiong Tan, Xiong Tan, Ruirui Wang, Ruirui Wang, Lu Zhi, Lu Zhi, } "Spectral–spatial classification of hyperspectral image using three-dimensional convolution network," Journal of Applied Remote Sensing 12(1), 016005 (4 January 2018). https://doi.org/10.1117/1.JRS.12.016005 . Submission: Received: 12 August 2017; Accepted: 13 December 2017
Received: 12 August 2017; Accepted: 13 December 2017; Published: 4 January 2018
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