Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time

Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images

This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time

Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms