A Depthwise Separable Fully Convolutional ResNet With ConvCRF for Semisupervised Hyperspectral Image Classification

Hyperspectral images classification relies on the accurate and efficient extraction of discriminative features, detail preservation, learning with limited training samples. This article, therefore, presents an advanced neural network architecture combined convolutional conditional random fields (ConvCRF) region growing (RGW) approaches to address these key issues. First, a depthwise separable fully residual (DFRes) is proposed for feature learning, where operation ensures larger field view, convolution can mitigate problem vanishing gradient overfitting. Second, because collection ground-truth labels usually difficult, integrates RGW method effectively overcome Third, ConvCRF used preserve image details fine-grained predictions. Finally, abovementioned components are coherently integrated into new semisupervised framework, i.e., DFRes RGW. Experimental results three hyperspectral datasets demonstrate that approach outperforms other state-of-the-art methods.