Sentinel-Land-Classification with DNN

Overview

This project focuses on leveraging Deep Neural Networks (DNNs) to classify different land types (such as agriculture, water, urban areas, desert, roads, and trees) using satellite imagery from the Sentinel-2 mission by the European Space Agency (ESA). The goal is to build an accurate classification model that can be useful for applications like urban planning, environmental monitoring, and resource management.

Dataset

Source: Sentinel-2 satellite images

Alternative Dataset: EuroSAT Dataset

Image Type: Multispectral images with different spectral bands (Red, Green, Blue, Near-Infrared, etc.)

Preprocessing: Resized, normalized, and enhanced using various techniques (e.g., atmospheric correction)

Project Milestones

1. Data Collection, Exploration & Preprocessing

Download Sentinel-2 images for the target region (e.g., Egypt) from Copernicus Open Access Hub

Perform Exploratory Data Analysis (EDA) to understand image composition and band relevance

Preprocess the dataset (resizing, band selection, image augmentation, and NDVI calculation)

Visualize spectral signatures of different land types

2. Advanced Data Analysis & Model Selection

Identify key spectral bands that influence land classification

Perform Principal Component Analysis (PCA) to reduce dimensionality

Experiment with various Deep Learning models, starting with CNNs (Convolutional Neural Networks)

Consider transfer learning using pre-trained models (e.g., ResNet, VGG, or U-Net)

3. Model Development & Training

Implement CNN-based DNN models using TensorFlow/Keras or PyTorch

Train the model with early stopping and hyperparameter tuning

Evaluate performance using metrics like accuracy, precision, recall, and F1-score

Visualize results using confusion matrices and activation maps

4. Deployment & Monitoring

Deploy the model as a web service/API using Flask or FastAPI

Monitor classification accuracy and detect model drift over time

Implement a strategy for periodic retraining with new satellite images

Installation & Usage

Requirements

Python 3.8+

TensorFlow / PyTorch

NumPy, Pandas, Matplotlib, Seaborn

OpenCV, Rasterio (for image processing)

Flask / FastAPI (for deployment)