SAR Image Change Detection

SAR image change detection technology allows for the comparison of SAR images taken at different times from identical geographical locations, followed by cluster analysis. This technique involves preprocessing steps such as radiometric calibration and speckle filtering, followed by pixel difference calculation or ratio-based change indicator generation. Implementation typically utilizes clustering algorithms like K-means or fuzzy C-means to categorize changed and unchanged regions. The technology enables better understanding of surface transformation processes and environmental evolution by identifying temporal variations across different areas. Based on clustering results, we can infer the causes and impacts of these changes through post-classification analysis. This methodology holds significant application value in geological research, environmental monitoring, and resource management fields, often implemented through MATLAB or Python libraries like scikit-learn for automated change mapping.