This repository contains MATLAB code for the classification of Hyper Spectral Images (HSIs) using various machine learning (ML) methods such as Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Multiple Linear Regression (MLR). Two datasets, namely Pavia and Indian Pines, are utilized for experimentation and evaluation.
The dataset comprises Hyper Spectral Images (HSIs) captured by remote sensing devices. Two main datasets are included:
- Pavia: Contains hyperspectral images captured over Pavia, Italy.
- Indian Pines: Contains hyperspectral images captured over the Indian Pines area in Indiana, USA.
The following ML methods are implemented for classification:
- Support Vector Machine (SVM): A supervised learning algorithm used for classification tasks by finding the hyperplane that best divides a dataset into classes.
- k-Nearest Neighbors (KNN): A simple and effective algorithm that classifies a data point based on the majority class among its k nearest neighbors.
- Random Forest (RF): A versatile ensemble learning method that builds multiple decision trees during training and outputs the mode of the classes for classification.
- Multiple Linear Regression (MLR): A statistical method for modeling the relationship between multiple independent variables and a dependent variable.
This project demonstrates the application of various ML methods for the classification of Hyper Spectral Images (HSIs) using MATLAB. Further experimentation and optimization can be explored to improve the classification accuracy and efficiency.