TY - GEN
T1 - Towards misregistration-tolerant change detection using deep learning techniques with object-based image analysis
AU - Liu, Tao
AU - Yang, Lexie
AU - Lunga, Dalton D.
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/11/5
Y1 - 2019/11/5
N2 - 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.
AB - 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.
KW - Change detection
KW - Convolutional neural network
KW - Deep learning
KW - Image misregistration
KW - LSTM
KW - OBIA
UR - http://www.scopus.com/inward/record.url?scp=85076953290&partnerID=8YFLogxK
U2 - 10.1145/3347146.3359068
DO - 10.1145/3347146.3359068
M3 - Conference contribution
AN - SCOPUS:85076953290
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 420
EP - 423
BT - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
A2 - Banaei-Kashani, Farnoush
A2 - Trajcevski, Goce
A2 - Guting, Ralf Hartmut
A2 - Kulik, Lars
A2 - Newsam, Shawn
PB - Association for Computing Machinery
T2 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
Y2 - 5 November 2019 through 8 November 2019
ER -