Sample Selection Based Change Detection with Dilated Network Learning in Remote Sensing Images
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The change detection system plays an active role in remote sensing images by detecting the changes in the image environment. Changes may be occur frequently due to the seasonal factors such as deforestation, natural disorders, etc. The objective of this paper is to develop an effective approach to find the changes that were experienced over a time period. The changes detected by difference image (DI) method is not efficient because it has the drawbacks in speckle noise suppression and classification, also the quality of DI affects the change map. This paper proposes a sample selection-multiple dilated convolutional neural network for the detection of changed and unchanged areas. In this, multiple dilation rates are incorporated, that overcome the existing gridding problems in network. The feature map determined at fully connected network extracts the global information by widening the receptive field that covers all the regions without any missing portions in the image. Hence, this type of architecture improves the learning in deep network by detecting the changes accurately. Finally, the trained network model achieves a feature map from the two images with the classified result of changed and unchanged pixels. The accuracy of the proposed change detection result provides improved results as compared with the existing algorithms. The performance metrics like false positive, false negative, overall error, the percentage of correct classification and Kappa are used for the estimation of the proposed image change detection task. Experiments are conducted on seven real datasets, and the result shows the better detection result than the other existing approaches.
KeywordsChange detection Remote sensing Convolutional neural network Supervised learning
- 1.Sharma, A., & Gulati, T. (2017). Change detection in remotely sensed images based on image fusion and fuzzy clustering. International Journal of Electronics Engineering Research, 9(1), 141–150.Google Scholar
- 7.Geng, J., Wang, H., Fan, J., & Ma, X. (2017). Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering. In 2017 International workshop on remote sensing with intelligent processing (RSIP) (pp. 1–3). IEEE.Google Scholar
- 20.Dong, C., Loy, C. C., He, K., & Tang, X. (2014, September). Learning a deep convolutional network for image super-resolution. In European conference on computer vision (pp. 184–199). Springer, Cham.Google Scholar