Automatic Change Detection in Synthetic Aperture Radar

Images Based on PCANet

IEEE Geoscience and Remote Sensing Letters, 13(12), 2016, December

Feng Gao, Junyu Dong, Bo Li, Qizhi Xu

Last modified: 24, March, 2017

Abstract: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy c-means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.

The MATLAB implementation can be requested from with tag [CD_PCANET_code] in the subject line. The demo has not been well organized. Please contact me if you meet any problems.

Links to most related works

  1. M. Gong, J. Zhao, J. Liu, Q. Miao, and L. Jiao, ※Change detection in synthetic aperture radar images based on deep neural networks,§ IEEE Trans. Neural Netw., vol. 27, no. 1, pp. 125每138, Jan. 2016.
  2. L. Jia et al., ※SAR image change detection based on iterative labelinformation composite kernel supervised by anisotropic texture,§ IEEE Trans. Geosci. Remote Sens., vol. 53, no. 7, pp. 3960每3973, Jul. 2015.
  3. T. Celik, ※Unsupervised change detection in satellite images using principal component analysis and k-means clustering,§ IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 772每776, Oct. 2009.
  4. M. Gong, L. Su, M. Jia, and W. Chen, ※Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images,§ IEEE Trans. Fuzzy Syst., vol. 22, no. 1, pp. 98每109, Feb. 2014.
  5. H.-C. Li, T. Celik, N. Longbotham, and W. J. Emery, ※Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering,§ IEEE Geosci. Remote Sens. Lett., vol. 12, no. 12, pp. 2458每2462, Dec. 2015.
  6. P. Zhang, M. Gong, L. Su, J. Liu, and Z. Li, ※Change detection based on deep feature representation and mapping transformation for multispatial-resolution remote sensing images,§ ISPRS J. Photogram. Remote Sens., vol. 116, pp. 24每41, Jun. 2016.
  7. T.-H. Chan, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, ※PCANet: A simple deep learning baseline for image classification?§ IEEE Trans. Image Process., vol. 24, no. 12, pp. 5017每5032, Dec. 2015.

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Last modified: 13, March, 2016.