Multispectral Satellite Data Classification using Soft Computing Approach
This work addresses the challenge of efficiently detecting clusters of varying sizes, shapes, and densities in multispectral satellite data, which is an incremental improvement for remote sensing applications.
The authors tackled the problem of clustering and classifying high-resolution multispectral satellite images by proposing a grid-density based clustering technique for object identification and a rule induction based machine learning algorithm for classification, with validation on synthetic and benchmark datasets.
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.