Crop Image Classification

This project involves classifying satellite images based on the Normalized Difference Vegetation Index (NDVI) using various neural network architectures. The goal is to categorize different land covers using satellite imagery.

Repo: github.com

  • Tech Stack: Python, Keras, TensorFlow, scikit-learn, GDAL

Key Features

  • Satellite image loading and preprocessing for use in machine learning models
  • Multiclass classification of satellite images using neural networks
  • Two different neural network architectures: a fully-connected network and a 1D Convolutional Neural Network
  • Evaluation of model performance using accuracy metrics

Evaluation Results

An accuracy of 95% was achieved.