Automatic analysis of the slight change image for unsupervised change detection

作者：Yang Jilian^*; Sun Weidong

来源：Journal of Applied Remote Sensing, 2015, 9(1): 095995.

DOI：10.1117/1.JRS.9.095995

• slight change detection
• k-means clustering
• autoregressive integrated moving average
• minimum mean absolute error
• locally linear embedding
• remote sensing

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.

• 出版日期2015-10-15
• 单位清华大学