ICA_SVD_GLCM_data

Volcanic deformation monitoring is crucial for understanding magmatic activity and assessing potential hazards. This project applies Independent Component Analysis (ICA) to multi-temporal InSAR (MT-InSAR) data to separate volcanic deformation signals from atmospheric noise and other error sources over Hawaii’s active volcanoes. By integrating ICA with Singular Value Decomposition (SVD) and Grey-Level Co-occurrence Matrix (GLCM), the study aims to enhance the accuracy of deformation signal extraction. The proposed method will be tested on SAR datasets from Sentinel-1 and other relevant missions, providing insights into long-term surface deformation patterns and improving early warning systems for volcanic activity in Hawaii.