3D/2D regularized CNN feature hierarchy for Hyperspectral image classification

• URL: http://arxiv.org/abs/2104.12136v1
• Date: Sun, 25 Apr 2021 11:26:56 GMT
• Title: 3D/2D regularized CNN feature hierarchy for Hyperspectral image classification
• Authors: Muhammad Ahmad, Manuel Mazzara, and Salvatore Distefano
• Abstract summary: Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) We propose an idea to enhance the generalization performance of a hybrid CNN for HSIC using soft labels. We empirically show that in improving generalization performance, label smoothing also improves model calibration.
• Score: 1.2359001424473932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Several regularization techniques have been used to overcome the aforesaid issues. However, sometimes models learn to predict the samples extremely confidently which is not good from a generalization point of view. Therefore, this paper proposed an idea to enhance the generalization performance of a hybrid CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels. The proposed method helps to prevent CNN from becoming over-confident. We empirically show that in improving generalization performance, label smoothing also improves model calibration which significantly improves beam-search. Several publicly available Hyperspectral datasets are used to validate the experimental evaluation which reveals improved generalization performance, statistical significance, and computational complexity as compared to the state-of-the-art models. The code will be made available at https://github.com/mahmad00.

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