Abstract
We present a novel auto-machine learning partial differential equations (PDE) driven deep learning framework for the classification of hyperspectral images (HSIs). The work is inspired by the famous PDE in image processing, namely, the Perona-Malik (PM) equation, which can form a scale-space and is capable of edge-preserving denoising using anisotropic diffusion (PM diffusion). In this framework, we firstly propose auto-machine learning-based trainable PM diffusion blocks (TPM-blocks) and then cascade them into a deep convolutional neural networks (CNN). Specifically, the $1 \times 1$ convolution layer and the trainable PM diffusion unit (TPMDU) are integrated as the TPM-block, and then multiple TPM-blocks are stacked to form a novel end-to-end deep learning architecture. We show that our deep learning method has the capacity of learning discriminative spectral and spatial features of HSIs. Experimental results on several popular datasets demonstrate that the proposed method achieves state-of-the-art performance compared with the several existing deep learning-based methods.
Published Version
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