New framework for hyperspectral change detection based on multi-level spectral unmixing

Earth is constantly changing due to some natural events and human activities that threaten our environment. Thus, accurate and timely monitoring of these changes is of great importance for properly coping with their consequences. In this regard, this research presented a new framework for hyperspectral change detection (HCD) based on dynamic time warping (DTW) and multi-level spectral unmixing. The proposed method included two parts. The first part provided the binary change map based on Otsu and DTW algorithms. The DTW algorithm plays the role of a robust predictor for HCD purposes and the Otsu algorithm selects the threshold for detecting change and no-change areas. The second part presented a multiple change map based on the local spectral unmixing procedure and the output of the image differencing (ID) algorithm. The second part, at the first step, uses the ID to predict change and no-change areas and then employs the binary change map for mask no-change pixels. The endmember estimation and extraction was applied to change pixels, and the correlation coefficient among the bands was calculated simultaneously. Next, change pixels were divided into many parts based on the correlation among the bands. In addition, the abundance map was estimated, and then the labeling process was applied for each part. Finally, the multiple change map was obtained by the fusion of the labels of all parts. The result of HCD was compared to those of other robust HCD methods by two real bi-temporal hyperspectral datasets. Based on the result of HCD in binary and multiple change maps, the proposed method had high performance compared to other methods and its overall accuracy and kappa coefficient were more than 90% and 0.77, respectively.