@inproceedings{ab5430bea64e4809aaf812a74edf8e69,
title = "An approach to multiple Change Detection in multisensor VHR optical images based on iterative clustering",
abstract = "When dealing with optical images, the most common approach to unsupervised change detection is Change Vector Analysis (CVA) which computes the multispectral difference image and exploits its statistical distribution in (hyper-)spherical coordinates. The latter step usually requires assumptions on both the model of class distributions and the number of changes. However, both assumptions are seldom satisfied especially when multisensor VHR images are considered. Thus, we propose an approach to multiple change detection in multisensor VHR optical images based on iterative clustering in (hyper-) spherical coordinate. The proposed approach is distribution free, unsupervised and automatically identifies the number of changes. Results obtained on a multitemporal and multisensor dataset including images from WorldView-2 and QuickBird are promising.",
keywords = "Change Detection, Clustering, Multisensor Images, Multitemporal Images, Region Growing, Very High Resolution",
author = "Solano-Correa, \{Yady Tatiana\} and Francesca Bovolo and Lorenzo Bruzzone",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
year = "2016",
month = nov,
day = "1",
doi = "10.1109/IGARSS.2016.7730342",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5149--5152",
booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",
address = "United States",
}