Remote Sensing Image Classification Description with MATLAB Implementation

This documentation presents remote sensing image classification using MATLAB implementation, including both code examples and sample images. Remote sensing image classification refers to the process of automatically categorizing and identifying different ground objects or scenes within remote sensing imagery using computer algorithms. The complete workflow involves several critical stages: image preprocessing, feature extraction, and classifier training. Through MATLAB programming, we implement various classification algorithms that process remote sensing data effectively. The code typically includes functions for image enhancement and normalization during preprocessing, texture and spectral feature extraction using techniques like GLCM (Gray-Level Co-occurrence Matrix) and PCA (Principal Component Analysis), and machine learning classifiers such as Support Vector Machines (SVM) or Random Forests. The implementation helps researchers and analysts better understand and interpret remote sensing image data by providing practical examples of how to apply computer vision techniques to geographical imagery. The MATLAB environment offers specialized toolboxes like the Image Processing Toolbox and Statistics and Machine Learning Toolbox that significantly streamline the development of classification pipelines for remote sensing applications.