Multispectral Satellite Data Classification using Soft Computing Approach

TL;DR

This paper presents a novel grid-density clustering method and a rule induction classification approach for multispectral satellite images, addressing high dimensionality and processing challenges.

Contribution

It introduces a new clustering technique and a rule-based classification method specifically designed for high-resolution multispectral satellite data.

Findings

Effective object detection demonstrated on synthetic datasets

Accurate classification achieved on benchmark datasets

Addresses high-dimensional data processing challenges

Abstract

A satellite image is a remotely sensed image data, where each pixel represents a specific location on earth. The pixel value recorded is the reflection radiation from the earth's surface at that location. Multispectral images are those that capture image data at specific frequencies across the electromagnetic spectrum as compared to Panchromatic images which are sensitive to all wavelength of visible light. Because of the high resolution and high dimensions of these images, they create difficulties for clustering techniques to efficiently detect clusters of different sizes, shapes and densities as a trade off for fast processing time. In this paper we propose a grid-density based clustering technique for identification of objects. We also introduce an approach to classify a satellite image data using a rule induction based machine learning algorithm. The object identification and classification methods have been validated using several synthetic and benchmark datasets.