ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 175–180, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-175-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 175–180, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-175-2021

  17 Jun 2021

17 Jun 2021

DEEP BAYESIAN ACTIVE LEARNING IN HIGH-RESOLUTION SATELLITE IMAGES FOR CHANGE DETECTION IN URBAN AND SUBURBAN AREAS

L. Ragia and A. Panagiotopoulou L. Ragia and A. Panagiotopoulou
  • Information Management Systems Institute, Athena Research Center, Artemidos 6, Marousi 151 25 Greece

Keywords: Bayesian CNN, Deep Active Learning, BALD, Dropout, QuickBird, WorldView, Change Detection

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.