Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3274
Title: Feature Fusion based Unsupervised Change Detection in Optical Satellite Images
Authors: Gupta, Neha
Singh, Pooja
Ari, Samit
Keywords: Binary change map
Canonical correlation analysis (CCA)
Change detection
Fuzzy c-means clustering
Gabor wavelet kernel
Multitemporal satellite image
Issue Date: Mar-2019
Citation: 3rd International Conference for Convergence in Technology (I2CT), Pune, India, 29-31 March 2019
Abstract: This 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.
Description: Copyright of this document belongs to proceedings publisher.
URI: http://hdl.handle.net/2080/3274
Appears in Collections:Conference Papers

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