A New Artificial Intelligence Optimization Method for PCA Based Unsupervised Change Detection of Remote Sensing Image Data

Title:

A New Artificial Intelligence Optimization Method for PCA Based Unsupervised Change Detection of Remote Sensing Image Data

Creator:

Atasever, U. H., Kesikoglu , M. H., and Ozkan, C.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:51a9850d-d2ed-4b27-9265-9d9d463b0410
uuid:51a9850d-d2ed-4b27-9265-9d9d463b0410
doi:10.14311/NNW.2016.26.008

Subject:

unsupervised change detection, differential search, and PCA

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this study, a new artificial intelligence optimization algorithm, Differential Search (DS), was proposed for Principal Component Analysis (PCA) based unsupervised change detection method for optic and SAR image data. The model firstly computes an eigenvector space using previously created k×k blocks. The change detection map is generated by clustering the feature vector as two clusters which are changed and unchanged using Differential Search Algorithm. For clustering, a cost function is used based on minimization of Euclidean distance between cluster centers and pixels. Experimental results of optic and SAR images proved that proposed approach is effective for unsupervised change detection of remote sensing image data.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2016 Volume:26 | Number:2

Harvested from:

CDK

Metadata only:

false