Band Selection via Explanations From Convolutional Neural Networks

Band Selection via Explanations From Convolutional Neural Networks

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Band Selection is a research hotspot in the field of hyperspectral imaging (HSI) processing.This paper proposes a method that selects bands for HSI classification by the explainability of a convolutional neural network (CNN).We design a CNN architecture and use its 1D gradient-weighted class activation mapping (GradCAM) to obtain a gradient-weighted heatmap (GradHM) by the last layer in the well-trained CNN.

Since the pooling layer in the CNN leads to a dimension change of the GradHM, cubic spline interpolation (CSI) is used to up-sample the GradHM.To further 30hh bikini improve the accuracy of the up-sampled GradHM for the important band, guided backpropagation was adopted to obtain a more detailed GradHM (Guided-GradHM).Finally, based on GradHM and Guided-GradHM, two strategies, named Average Selection (AS) and Total Selection (TS), are proposed to form new and different combinations of methods (GradHM+AS, GradHM+TS, Guided-GradHM+AS, and Guided-GradHM+TS).

In experiments, the proposed methods show better performance compared to other methods.In most cases, if the selected bands were fewer, skull bride and groom then the Guided-GradHM+AS was more credible than the other methods.Furthermore, different datasets were used to validate the proposed methods.