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Depthwise Separable ResNet in the MAP Framework for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2020.3033149.png)
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Abstract
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To build small and efficient neural networks for hyperspectral image (HSI) classification, this letter presents a depthwise separable residual neural network (ResNet). This approach, motivated by the popular MobileNet architecture, decomposes the traditional spatial-spectral convolution operation into a spatial-independent pointwise spectral convolution and a spectral-independent depthwise spatial convolution. It allows the separation of spectral and spatial information in HSI and also greatly reduces the network size to prevent the overfitting issue. To better preserve the class boundaries and edges, the proposed ResNet is integrated into a maximum a posteriori (MAP) framework to allow the use of the conditional random field (CRF) model. The experiment results on benchmark HSI scene demonstrate that the proposed ResNet compares favorably with several popular deep learning HSI classifiers and that the ResNet-CRF approach achieves higher accuracy and better boundaries among neighboring classes.
Keywords:
Convolution
Training
Feature extraction
Residual neural networks
Principal component analysis
Hyperspectral imaging
Solid modeling
Conditional random field (CRF)
graph-cut (GC)
hyperspectral image (HSI) classification
maximum a posteriori (MAP)
residual neural network (ResNet)
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