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RMCNet: Random Multiscale Convolutional Network for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2020.3007433.png)
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Abstract
En 中文
To address the limitation of the high-dimensionality features and single spatial scale in the spectral-spatial classification of hyperspectral image (HSI), we propose a random multiscale convolutional network (RMCNet) that combines a multiscale dimensionality reduction module (MDRM) and the RMCNet for improving classification accuracy. The MDRM is based on multiscale superpixel segmentations, which implements dimensionality reduction leading to relieve the Hughes problem and reduce the computation burden in deep learning. Then, the multiscale spectral-spatial features are extracted by the RMCNet to adaptive various complex scenes in HSI. Finally, the multiscale spectral-spatial features act as inputs of support vector machine for classification. In the experiments, three benchmark HSIs are used to evaluate the performance of the proposed method. The experimental results demonstrate that the RMCNet can yield a competitive performance compared with the state-of-the-art methods.
Keywords:
Feature extraction
Convolution
Dimensionality reduction
Principal component analysis
Kernel
Training
Hyperspectral sensors
Dimensionality reduction
feature extraction
hyperspectral image (HSI) classification
random multiscale convolutional network (RMCNet)
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