TIFF Image PCA Analysis Tool

A graphical user interface (GUI) application that analyzes TIFF images using PCA (Principal Component Analysis).

Features

• TIFF Image Support: Load multi-band and single-band TIFF files
• PCA Analysis: Perform principal component analysis on image bands
• Visualization:
  • Original image preview (RGB/grayscale)
  • First principal component (PC1) map
  • Value visualization with color scale
• Statistics Table: For each component:
  • Explained variance (Eigenvalue)
  • Variance ratio (%)
  • Standard deviation

Installation

Requirements

• Python 3.7+
• Virtual environment (recommended)

Steps

1. Clone or download the project
2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # macOS/Linux
# or
venv\Scripts\activate  # Windows

3. Install required libraries:

pip install numpy scikit-learn pillow matplotlib tifffile

Usage

Start the application:

python main.py

Step-by-Step Usage

1. When the application opens, click the "Load TIFF Image" button
2. Select a TIFF file (.tif or .tiff)
3. PCA analysis is performed automatically
4. Results are displayed visually and in a table:
   • Left panel: Original image
   • Right panel: PC1 component
   • Bottom table: Statistics for each component

Technical Details

Supported Formats

• TIFF (.tif, .tiff)
• Single-band (grayscale) images
• Multi-band (multi-band/hyperspectral) images

PCA Process

1. Image is flattened (H×W×C → (H×W)×C)
2. Maximum 10 components are calculated
3. First component (highest variance) is visualized
4. Statistics for all components are displayed in the table

Libraries

• tkinter: GUI framework
• numpy: Numerical operations
• scikit-learn: PCA algorithm
• PIL/Pillow: Image processing
• matplotlib: Data visualization
• tifffile: Advanced TIFF file support

Project Structure

PCA/
├── main.py              # Main application file
├── README.md            # This file
└── venv/                # Virtual environment (optional)

Troubleshooting

"Cannot identify image file" error

This error may be caused by PIL's limitations with certain TIFF formats. The code automatically switches to using the tifffile library in such cases.

Warning on grayscale images

PCA provides limited information on single-band images. It is recommended to use multi-band images.

License

This project was developed for educational purposes.

Contributing

You can open pull requests for suggestions and improvements.