  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.
