Hyperspectral Image Classification with CNN and AE by Lalan KumarHyperspectral Image Classification with CNN and AE by Lalan Kumar

Hyperspectral Image Classification with CNN and AE

Lalan Kumar

Completed work

ML Engineer

Python

PyTorch

TensorFlow

Geospatial

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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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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