Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification

• URL: http://arxiv.org/abs/2205.09250v1
• Date: Thu, 19 May 2022 00:02:16 GMT
• Title: Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification
• Authors: Mohammad Joshaghani, Amirabbas Davari, Faezeh Nejati Hatamian, Andreas Maier, Christian Riess
• Abstract summary: We use a special class of deep neural networks, namely Bayesian neural network, to classify HSRS images. Bayesian neural networks provide an inherent tool for measuring uncertainty. We show that a Bayesian network can outperform a similarly-constructed non-Bayesian convolutional neural network (CNN) and an off-the-shelf Random Forest (RF)
• Score: 14.464344312441582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Employing deep neural networks for Hyper-spectral remote sensing (HSRS) image classification is a challenging task. HSRS images have high dimensionality and a large number of channels with substantial redundancy between channels. In addition, the training data for classifying HSRS images is limited and the amount of available training data is much smaller compared to other classification tasks. These factors complicate the training process of deep neural networks with many parameters and cause them to not perform well even compared to conventional models. Moreover, convolutional neural networks produce over-confident predictions, which is highly undesirable considering the aforementioned problem. In this work, we use a special class of deep neural networks, namely Bayesian neural network, to classify HSRS images. To the extent of our knowledge, this is the first time that this class of neural networks has been used in HSRS image classification. Bayesian neural networks provide an inherent tool for measuring uncertainty. We show that a Bayesian network can outperform a similarly-constructed non-Bayesian convolutional neural network (CNN) and an off-the-shelf Random Forest (RF). Moreover, experimental results for the Pavia Centre, Salinas, and Botswana datasets show that the Bayesian network is more stable and robust to model pruning. Furthermore, we analyze the prediction uncertainty of the Bayesian model and show that the prediction uncertainty metric can provide information about the model predictions and has a positive correlation with the prediction error.

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