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.

An approach to multiple Change Detection in multisensor VHR optical images based on iterative clustering

Solano Correa, Yady Tatiana;Bovolo, Francesca;
2016-01-01

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.
2016
978-1-5090-3332-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/307148
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