Change Detection in Hyperspectral Images Using Deep Feature Extraction and Active Learning

Document Type

Conference Article

Publication Title

Communications in Computer and Information Science

Abstract

Manual labelling of changes present in a pair of remotely sensed hyperspectral images is costly and time-consuming. As the label information is less, one might take an active learning approach where the machine learning model can learn with smart human supervision. However, there is a lack of research in the literature around change detection in partially labelled hyperspectral images. This article proposes a convolutional autoencoder-based model for detecting changes in hyperspectral images, which would reduce the data’s dimensionality and learn from unlabelled samples. The final classifier model has been re-trained using active learning. After each epoch, the model builds a decision boundary and automatically picks samples for manual labelling based on an uncertainty parameter modelled using the beta distribution function. The selected pixels’ label information is fed into the model to improve the accuracy of change detection, and the model is iterated a number of times by adding the labelled examples to the training set. Starting with a small and non-optimal training set, the model is permitted to query for the labels of k most uncertain samples at each iteration to build the updated decision boundary. It has been seen that the optimal decision boundary could be constructed by fewer labelled samples only and thus eliminating the requirement for a huge training set. According to the results, the suggested model needs extremely minimal training data (only 17.14 % and 18.57 % of training data for Bay Area and Santa Barbara images respectively) to obtain a comparatively higher level of performance.

First Page

212

Last Page

223

DOI

10.1007/978-981-99-1648-1_18

Publication Date

1-1-2023

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