Satellite Image Classification

This project focuses on classifying satellite images into different land cover categories using deep learning models.

Project Overview

The main objective of this project is to develop and evaluate machine learning models that can accurately classify satellite images into predefined classes such as:

• Cloudy
• Desert
• Green Area
• Water

Models Used

The project implements and compares multiple state-of-the-art deep learning models:

• ResNet-50
• EfficientNetB2
• MobileNetV2

Project Workflow

1. Data Preparation:

   • Data is loaded and preprocessed from the dataset directory.
   • Images are categorized into the aforementioned classes.

2. Model Training:

   • Models are trained on the dataset with an 80-10-10 split for training, validation, and testing respectively.
   • Multiple epochs are used to ensure the models learn effectively.

3. Evaluation:

   • The models are evaluated on the validation and test sets to assess their accuracy and performance.
   • The best performing model is selected based on validation accuracy.

Results

The MobileNetV2 model achieved an accuracy of 96.27% on the validation dataset.

Requirements

• TensorFlow
• NumPy
• Matplotlib
• Pandas

How to Run

1. Clone the repository.
2. Install the required libraries.
3. Run the Jupyter notebook file to train and evaluate the models.