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Open AccessArticle

Depth-Wise Separable Convolution Neural Network with Residual Connection for Hyperspectral Image Classification

by Lanxue Dang 1,2,3, Peidong Pang 1,2 and Jay Lee 4,5,*
1
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
2
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China
3
Henan Engineering Laboratory of Spatial Information Processing, Henan University, Kaifeng 475004, China
4
College of Environment and Planning, Henan University, Kaifeng 475004, China
5
Department of Geography, Kent State University, Kent, OH 44240, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3408; https://doi.org/10.3390/rs12203408
Received: 13 September 2020 / Revised: 14 October 2020 / Accepted: 15 October 2020 / Published: 17 October 2020
(This article belongs to the Section Remote Sensing Image Processing)
The neural network-based hyperspectral images (HSI) classification model has a deep structure, which leads to the increase of training parameters, long training time, and excessive computational cost. The deepened network models are likely to cause the problem of gradient disappearance, which limits further improvement for its classification accuracy. To this end, a residual unit with fewer training parameters were constructed by combining the residual connection with the depth-wise separable convolution. With the increased depth of the network, the number of output channels of each residual unit increases linearly with a small amplitude. The deepened network can continuously extract the spectral and spatial features while building a cone network structure by stacking the residual units. At the end of executing the model, a 1 × 1 convolution layer combined with a global average pooling layer can be used to replace the traditional fully connected layer to complete the classification with reduced parameters needed in the network. Experiments were conducted on three benchmark HSI datasets: Indian Pines, Pavia University, and Kennedy Space Center. The overall classification accuracy was 98.85%, 99.58%, and 99.96% respectively. Compared with other classification methods, the proposed network model guarantees a higher classification accuracy while spending less time on training and testing sample sites. View Full-Text
Keywords: convolution neural network; depth-wise separable convolution; residual unit; hyperspectral image classification; spatial-spectral features convolution neural network; depth-wise separable convolution; residual unit; hyperspectral image classification; spatial-spectral features
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MDPI and ACS Style

Dang, L.; Pang, P.; Lee, J. Depth-Wise Separable Convolution Neural Network with Residual Connection for Hyperspectral Image Classification. Remote Sens. 2020, 12, 3408.

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