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Abstract This paper presents an approach for the classification of satellite images by recognizing various objects in them. Satellite images are rich in geographical information that can be used in a number of useful ways. The proposed system classifies satellites images by extracting different objects from the images. Our object recognition mechanism extracts attributes from satellite images under two domains namely: color pixels’ organization and pixel intensity. The extracted attributes aid in the identification of objects lying inside the satellite images. Once we are able to identify objects, we proceeded further to classify satellite images with the help of decision trees. The system has been tested for a number satellite images acquired from around the globe. The objects in the images have been further subdivided into different sub categories to improve the classification and prediction process. This is a novel approach which is not using any image processing techniques but is utilizing the extracted features to identify objects and then using these objects to classify the satellite images.

M. Shahbaz (B) Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan e-mail: [email protected] A. Guergachi Information Technology Management, Ted Rogers School, Ryerson University, Toronto, ON, Canada e-mail: [email protected] A. Noreen Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan e-mail: [email protected] M. Shaheen Department of Computer Science, FAST NU, Peshawar, Pakistan e-mail: [email protected] G.-C. Yang et al. (eds.), IAENG Transactions on Engineering Technologies, Lecture Notes in Electrical Engineering 229, DOI: 10.1007/978-94-007-6190-2_17, © Springer Science+Business Media Dordrecht 2013

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Keywords Classification of images · Data mining learning · Object recognition · Satellite images

M. Shahbaz et al.

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Decision tree

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Machine

1 Introduction Remotely sensed images are rich in geographical information by capturing various geographical objects. Geographical information can be useful for different sectors like government, business, science, engineering and research institutes. Geographical information can be used for planning, extraction and analysis of natural resources and help improve the vegetation of an area. These are few examples but there can be gazillions of its advantages. Remotely sensed images that we can acquire through satellites, sensors and radars are very large in numbers and in size as well. With the advancement of technologies, such as image digitization and storage, quantity of images has also elevated [1]. Each image has huge information residing inside it in the form of objects. It is difficult for humans to go through each image and extract patterns from such images. With the help of state of the art image storage, data analysis and classification techniques it is possible to automate who process to understand hidden patterns and help improve the predict

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