Abstract
Abstract. In this work the problem of change detection in high-resolution (HR) satellite images is addressed. The active learning (AL) algorithm Bayesian active learning disagreement (BALD) is applied on WorldView images of urban and suburban areas in the island of Crete, Greece. Comparisons with results from random sampling (RS) on AL are carried out. Several cases of selecting different amounts of images in the training set of a convolutional neural network (CNN) are experimented. The results show that the validation accuracy of classification as changed or unchanged of the BALD algorithm is superior to that of the RS algorithm. Indeed, the BALD algorithm achieves zero test error against the test errors 34.6% and 38.5% of the RS algorithm. Actually, as the amount of training images increases, the accuracy also increases. Interesting experiments could be executed in the future utilizing estimators from robust statistics inside the AL acquisition function framework. Up to now in the literature no other work has appeared to present deep AL on WorldView images for change detection.
Highlights
A significant challenge in remote sensing (RMSS) applications is obtaining labelled data as changed or unchanged (Ruzicka et al, 2020)
A convolutional neural network (CNN) model is trained on the WorldView images by various cases of selecting different amounts of images in the training set
The validation accuracy increases as the number of training images increases
Summary
A significant challenge in remote sensing (RMSS) applications is obtaining labelled data as changed or unchanged (Ruzicka et al, 2020). Maps over huge areas have to be kept up-to-date by being renewed with gradual renovations. Data which have been acquired across aerial or satellite surveys serve for recognizing the potential changes which have to be introduced into the map. Change detection through automatic image analysis is a problem needed to be addressed in RMSS. Machine learning (ML) and in specific active learning (AL) can face the above-mentioned challenge of change detection in RMSS. In AL frameworks a system has the potential to learn from small amounts of data and choose unattended what data it would prefer to be labelled by the user. Cost and time can be saved when training a ML system via AL, due to the reduced amount of required labelling
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