Abstract
Image change detection is a process that analyzes images of the same scene taken at different times in order to identify changes that may have occurred between the multitemporal images. This letter proposes a remote sensing image change detection algorithm based on BM3D and PCANet. Firstly, the BM3D algorithm is utilized to remove the noise in the log-ratio image, then the gray level co-occurrence matrix (GLCM) and FCM algorithm are utilized to select the image patches which are used to train the PCANet model. Finally the pixels in the multitemporal images are classified by the trained PCANet model, the changed and unchanged pixels are combined to form the final change map. The experimental results obtained in this letter verify the effectiveness of the proposed algorithm.
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Wan, Y., Liu, Y., Peng, Q., Jie, F., Ming, D. (2019). Remote Sensing Image Change Detection Algorithm Based on BM3D and PCANet. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_50
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DOI: https://doi.org/10.1007/978-981-13-9917-6_50
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