Active learning with Bayesian CNN using the BALD method for Hyperspectral Image Classification

Abstract

Deep learning DL techniques have recently been used to examine the classification of remote sensing data like hyperspectral images HSI. However, DL models are difficult to obtain since they rely largely on a large number of labeled training data. Therefore, a current challenge in the field of HSI classification is how to effectively incorporate DL models in constrained labeled data. The Bayesian Convolutional Neural Networks BCNN method is robust against overfitting on small datasets. One of the key methods for automating data selection is active learning AL, which has gained popularity in recent decades. By choosing the most informative samples, AL aims to reduce the costly data labeling procedure and build a robust training set that is resource-efficient. In this work, we aim to improve the performance of BCNN using AL method to build a competitive classifier considering the Bayesian Active Learning Disagreement BALD acquisition function (Dropout Bayesian Active Learning by Disagreement), which incorporates model uncertainty information. In a previous work, BCNN was built and applied on Pavia datasets giving 99.7% classification accuracy. For comparison traditional BCNN with BALD, The techniques were applied on the Indian Pines dataset. The average accuracy of the classification had increased from 90% to 98% using BALD method.