Hyperspectral Image Classification with CNN and AE

Lalan Kumar

Completed work

ML Engineer

Python

PyTorch

TensorFlow

Geospatial

Hyperspectral Image Classification (HIC)

🌐 Project Overview

Hyperspectral Image Classification (HIC) is an advanced machine learning project designed to extract meaningful insights from hyperspectral imagery using state-of-the-art deep learning techniques. This workflow combines Convolutional Neural Networks (CNN) and Autoencoder (AE) models to provide robust and accurate classification of hyperspectral data.

🔬 Scientific Background

Hyperspectral imaging captures electromagnetic radiation across numerous contiguous spectral bands, providing rich spectral information beyond traditional RGB imaging. This project addresses critical challenges in:

Land use classification

Agricultural monitoring

Environmental research

Remote sensing applications

Example Images

🏗️ Project Architecture

Key Components

Data Processing

Data Ingestion: Sophisticated loading and preprocessing of complex hyperspectral datasets

Data Transformation: Advanced feature extraction and normalization techniques

Machine Learning Models

Convolutional Neural Network (CNN):

Specialized architecture for spatial-spectral feature learning

High-performance classification across multiple spectral domains

Autoencoder (AE):

Dimensionality reduction

Feature representation learning

Noise reduction and data compression

Computational Pipelines

Training Pipeline: End-to-end model training workflow

Prediction Pipeline: Seamless inference and classification

📂 Project Structure

HIC/
│
├── DATA/                   # Raw hyperspectral image datasets
│   ├── Botswana/           # Regional hyperspectral dataset
│   │   ├── Botswana.mat      # Hyperspectral image data
│   │   └── Botswana_gt.mat   # Ground truth labels
│   └── KSC/                # Kennedy Space Center dataset
│       ├── KSC.mat           # Hyperspectral image data
│       └── KSC_gt.mat        # Ground truth labels
│
├── artifacts/              # Project output
│   ├── trained_models/         
│   ├── visualizations/        
│   └── results/
│
├── config/                 # Configuration management
│   └── config.yaml            # Project configuration file
│
├── logs/                   # Execution and debugging logs
│
└── src/                    # Source code
    ├── __init__.py
    ├── exception.py           # Custom exception handling
    ├── logger.py             # Logging configuration
    ├── utils.py              # Utility functions
    │
    ├── components/         # Data processing modules
    │   ├── __init__.py
    │   ├── data_ingestion.py       # Data loading and preprocessing
    │   ├── data_transformation.py  # Feature extraction and normalization
    │   └── model_trainer.py        # Model training utilities
    │
    ├── models/             # Neural network architectures
    │   ├── __init__.py
    │   ├── ae_model.py            # Autoencoder model definition
    │   └── cnn_model.py           # CNN model definition
    │
    └── pipeline/           # Machine learning workflows
        ├── __init__.py
        ├── predict_pipeline.py      # Inference and prediction workflow
        └── train_pipeline.py        # Model training workflow

🚀 Getting Started

Prerequisites

Python 3.8+

Machine learning libraries (NumPy, Pandas, Scikit-learn)

Deep learning framework (TensorFlow/PyTorch)

CUDA-compatible GPU (recommended)

Installation

# Clone the repository
git clone https://github.com/your-username/HyperSpectral-AI.git

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Quick Start

# Train the model
python -m src.pipeline.train_pipeline

# Run predictions
python -m src.pipeline.predict_pipeline

📊 Supported Datasets

Botswana Hyperspectral Dataset

Geographic region: Southern Africa

Spectral characteristics: High-resolution land cover classification

Kennedy Space Center (KSC) Hyperspectral Dataset

Geographic region: Florida, United States

Applications: Coastal and environmental monitoring

Pavia University (PaviaU) Dataset

Geographic region: Northern Italy

Applications: Urban area classification

Pavia Centre (PaviaC) Dataset

Geographic region: Pavia city center, Italy

Applications: Urban material and structure identification

Indian Pines Dataset

Geographic region: Northwestern Indiana, United States

Applications: Agricultural area classification

💡 Pro Tip: Add your custom hyperspectral datasets to the DATA/ directory

🛠️ Development Principles

Object-oriented design

Modular architecture

PEP 8 and PEP 257 compliance

Comprehensive error handling

Detailed logging

🤝 Contributing

Fork the repository

Create your feature branch (git checkout -b feature/AmazingFeature)

Commit your changes (git commit -m 'Add some AmazingFeature')

Push to the branch (git push origin feature/AmazingFeature)

Open a Pull Request

📜 License

MIT License

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Completed work

Posted Jul 30, 2025

Advanced ML project for hyperspectral image classification using CNN and AE models.

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