Remote Sensing Applications and Decision Support

Automatic analysis of the slight change image for unsupervised change detection

[+] Author Affiliations
Jilian Yang, Weidong Sun

Tsinghua University, Department of Electronic Engineering, Beijing 100084, China

J. Appl. Remote Sens. 9(1), 095995 (Oct 15, 2015). doi:10.1117/1.JRS.9.095995
History: Received July 14, 2014; Accepted August 27, 2015
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Abstract.  We propose an unsupervised method for slight change extraction and detection in multitemporal hyperspectral image sequence. To exploit the spectral signatures in hyperspectral images, autoregressive integrated moving average and fitting models are employed to create a prediction of single-band and multiband time series. Minimum mean absolute error index is then applied to obtain the preliminary change information image (PCII), which contains slight change information. After that, feature vectors are created for each pixel in the PCII using block processing and locally linear embedding. The final change detection (CD) mask is obtained by clustering the extracted feature vectors into changed and unchanged classes using k-means clustering algorithm with k=2. Experimental results demonstrate that the proposed method extracts the slight change information efficiently in the hyperspectral image sequence and outperforms the state-of-the-art CD methods quantitatively and qualitatively.

© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Jilian Yang and Weidong Sun
"Automatic analysis of the slight change image for unsupervised change detection", J. Appl. Remote Sens. 9(1), 095995 (Oct 15, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095995


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