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dc.contributor.authorGupta, Neha-
dc.contributor.authorSingh, Pooja-
dc.contributor.authorAri, Samit-
dc.identifier.citation3rd International Conference for Convergence in Technology (I2CT), Pune, India, 29-31 March 2019en_US
dc.descriptionCopyright of this document belongs to proceedings publisher.en_US
dc.description.abstractThis paper proposes a feature fusion technique for unsupervised change detection. Features extracted from two different techniques are fused to get the final feature vectors. The first technique utilizes the Gabor wavelet at multiple orientations and scales, where maximum magnitude over all orientation in each scale is taken to create features of two multitemporal satellite images. The second technique applies canonical correlation analysis (CCA) on the combination of original multispectral bands and extracted local neighborhood information from all the bands. Next, the difference feature vectors obtained from individual techniques are fused to generate the final feature vectors. Furthermore, to get the binary change map, fuzzy c-means clustering is applied on final extracted features. In this feature fusion, the local neighborhood information from Gabor wavelet kernel is combined with joint change information from group of pixels extracted by CCA to produce more discriminant features.Experiments conducted on optical satellite images, which are collected by two sensors of Landsat satellite, and it shows the better performance of the proposed technique compared to earlier stated techniques.en_US
dc.subjectBinary change mapen_US
dc.subjectCanonical correlation analysis (CCA)en_US
dc.subjectChange detectionen_US
dc.subjectFuzzy c-means clusteringen_US
dc.subjectGabor wavelet kernelen_US
dc.subjectMultitemporal satellite imageen_US
dc.titleFeature Fusion based Unsupervised Change Detection in Optical Satellite Imagesen_US
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