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Towards Misregistration-Tolerant Change Detection using Deep Learning Techniques with Object-Based Image Analysis...

by Tao Liu, Hsiuhan Yang, Wadzanai D Lunga
Publication Type
Conference Paper
Book Title
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Publication Date
Page Numbers
420 to 423
Conference Name
27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019)
Conference Location
Chicago, Illinois, United States of America
Conference Sponsor
Microsoft, Apple, Here, ESRI, DiDi
Conference Date
-

Co-registrating is a common pre-processing step for existing change detection algorithms, but registering bi-temporal images is nontrivial. The use of image patch as input for deep learning techniques provides a natural avenue to apply them in the OBIA framework, and have shown successful performance in the object-based land cover mapping and change detection applications. Even though attempts of applying deep learning techniques for change detection applications have been made with varying success, its application under OBIA framework for change detection have not been conducted and its tolerance for misregistration among temporal images are neither known. This study performed change detection under OBIA framework using deep learning techniques for the first time, and evaluated its performance regarding their tolerance of image misregistration on training and testing dataset. Our results demonstrate the proposed change detection scheme is surprisingly robust to image misregistration on the testing dataset, while classifiers trained with the training dataset containing image misregistration errors suffer from slight decrease of overall accuracy.