2017 IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2017)

287 / 2017-02-01 00:46:07

Unsupervised Change Detection in Optical Satellite Images using Binary Descriptor

Change detection, multitemporal satellite image, local binary similarity pattern (LBSP), binary change map

In this paper, a novel unsupervised technique is proposed to get the change analysis of multitemporal satellite images. The proposed technique is based on the local binary similarity pattern (LBSP) concept. In this binary descriptor, inter-LBSP is used to detect the changes. In this approach, the main challenge is to calculate the threshold which is used to generate the binary feature vectors. Here, an effective solution has been found, where the neighbourhood information is used for calculation of threshold. The calculated threshold is used to obtain binary
feature vectors for each pixel. Hamming distance is used as a similarity metric to compare the binary vectors of each image for each pixel position which gives the binary change map of changed and unchanged region. Optical satellite images acquired by Landsat satellite are used to perform the experiments. Experimental results show that the proposed method provides better results compared to earlier reported techniques like expectation maximization and kernel k-means methods.