Accelerated Implementation of Kohonen Self Organising Map for Remote Sensing of Satellite Images
This project showcases an optimized Numba-JIT accelerated Pythonic implementation of the Kohonen Self-Organizing Map (SOM) with customizable grid matrix sizes, designed for multispectral satellite image processing. The system takes multispectral satellite images as input and generates coded images using the trained SOM as a codebook, all conveniently packaged as a Python executable. Furthermore, the project includes image restoration capabilities, enabling a comparison with the original image, and provides vivid data visualization through informative plots. The implementation allows users to set parameters such as the SOM dimensions, initial learning rate, maximum iterations, and neighborhood function spread factor for fine-tuned control during execution.