MATLAB Implementation of Remote Sensing Image Classification

This project provides MATLAB source code and sample images for remote sensing image classification. The implementation includes comprehensive post-classification evaluation metrics such as overall accuracy, Kappa coefficient, and confusion matrix analysis.

The primary objective of this project is to classify remote sensing imagery to better understand and analyze surface features. The MATLAB source code enables automated classification processes, significantly improving both accuracy and efficiency. The classification algorithm employs image processing techniques including feature extraction, pixel-based classification methods, and potentially machine learning approaches for pattern recognition.

Post-classification evaluation is implemented through specialized functions that calculate key performance indicators: overall accuracy measures the percentage of correctly classified pixels, Kappa coefficient assesses classification agreement beyond chance, and confusion matrix provides detailed class-wise performance analysis. These evaluation metrics allow researchers to objectively assess algorithm performance, determine classification reliability, and establish benchmarks for subsequent research applications. The code includes visualization functions to display classification results and evaluation metrics in comprehensive output reports.