Journal

Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods

This paper explores machine learning techniques for automatic change detection in remote sensing, addressing the need for efficient environmental monitoring. It compares post-classification comparison using a decision tree algorithm with a separability matrix against image differencing based on algebraic techniques. Landsat satellite data is used for pixel-wise computation, and performance is validated through 10-fold cross-validation. Results indicate that the post-classification method achieves higher accuracy, demonstrating its effectiveness in improving change detection and classification performance.