Hojjatollah Mahboobi; Mohsen Azadbakht
Abstract
Introduction: In many studies concerning monitoring the water surface changes, multi-temporal images are separately analyzed and after extracting water boundaries in each image, these boundaries are compared and the changes are determined. Nevertheless, there is a demand for methods that can perform ...
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Introduction: In many studies concerning monitoring the water surface changes, multi-temporal images are separately analyzed and after extracting water boundaries in each image, these boundaries are compared and the changes are determined. Nevertheless, there is a demand for methods that can perform accurately as well as facilitating the identification of changes. Therefore, to this end, in this research synergy of multi-temporal image fusion methods and classification methods was investigated detect surface water changes in Maharlu Lake between 2013 and 2018. Material and methods: After performing the necessary pre-processing, the Gram-Schmidt (GS) and Principal Component Analysis (PCA) methods were applied to fuse images and, then, changed and unchanged areas were extracted through applying classification methods to the fused images. Support Vector Machine (SVM) and Maximum Likelihood (ML) were used to classify fused images. In the next step, combinations of these methods were compared to each other and the best pair was extracted. Finally, the selected pair was compared with conventional change detection methods. Results and discussion: The results showed that based on the GS-SVM methods, the Maharlu Lake retreated about 163.3 km2 from 2013 to 2018. For accuracy assessment of the methods, the overall accuracy and Kappa coefficient were calculated. The GS-SVM method had an overall accuracy of 99.33%, Kappa coefficient of 0.99 and a relative error of 3.92 km2. This pair detected changes more accurately and the results were closer to reality. In the next step, the water surface was extracted from the images using conventional change detection methods, such as image differencing, band rationing, and NDVI differencing, and their results were compared to that of the GS-SVM. According to the results, the GS-SVM compared with other methods had higher overall accuracy and Kappa coefficient, and simultaneously, the least relative error. Conclusion: The results of this study showed that a combination of the GS image fusion method and SVM classifier provides satisfactory results to extract changes from multi-temporal images. This synergy can be used as an effective tool for detecting changes, particularly since fusing images can also be effective in improving classification accuracy by enhancing the spatial resolution of images.
