Abstract
In this paper we propose an unsupervised approach based on low-rank representation (LRR) for change detection in remote sensing images. Given a pair of remote sensing images obtained from the same area but in different time, the subtraction and logarithm ratio operators are firstly applied to obtain two difference images. Meanwhile the sparse part generated by LRR is also employed for acquiring another difference image, which can detect the change information. Afterwards, LRR is used again to obtain the low-rank part of these three difference images which can reflect the common characteristics. Finally k-means is performed on the low-rank part and thus the final result of change detection can be gained. Experimental results show the effectiveness and feasibility of the proposed method.
The project is supported by National Natural Science Foundation of China (61074029) and Natural Science Foundation of Liaoning Province (20102014)
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Cheng, Y., Jiang, Z., Shi, J., Zhang, H., Meng, G. (2014). Remote Sensing Image Change Detection Based on Low-Rank Representation. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_37
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DOI: https://doi.org/10.1007/978-3-662-45498-5_37
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