Land Cover Detection Using Machine Learning Project Overview
Objective: To implement a land cover detection system using machine learning algorithms on a dataset of remotely sensed images, aiming to classify different types of land cover with high accuracy.
Key Components:
- Data Preparation: The project utilizes the UC Merced Land Use dataset, comprising 2100 images across 21 land cover categories. The dataset includes both high and low-resolution RGB images, with a primary focus on using image features for classification.
- Methodology:
- Dataset Handling: The images are shuffled and divided into 80% training, 10% validation, and 10% testing samples.
- Feature Extraction & Visualization: Utilizing provided code to compute image features and visualize images, with an emphasis on Histogram of Oriented Gradients (HOG) for feature representation.
- Machine Learning Application: Employing a variety of machine learning algorithms, including K-Means, Gaussian Mixture Models, Support Vector Machine, and Neural Networks, to classify images into distinct land cover categories.
- Model Evaluation: Conducting thorough testing and validation, using metrics like accuracy and confusion matrices to assess model performance.
Key Outcomes:
- Effective Classification: Successfully categorized images into distinct land cover types, leveraging the strengths of various machine learning models.
- Insightful Data Analysis: Deep analysis of the dataset revealed significant patterns and relationships in land cover types, contributing to the field of remote sensing and environmental monitoring.
- Advanced Computational Techniques: Demonstrated the efficacy of using high-dimensional data in machine learning, showcasing the potential for similar applications in other domains.
Explore More: For a detailed exploration of the project’s methodology and findings, visit the GitHub repository: Land Cover Detection Project
