Automatic extraction of buildings and change detection of buildings from satellite images is an important tool for city management and planning. The discovery of changes is the process of identifying differences in the state of the objects extracted from the remote image by observing different time periods. The main objective of this paper is to extract the manmade objects (buildings) from the given input satellite images and detect the changes in the extracted building map. This work presents the Region of Interest (ROI) and extraction of the building using K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) techniques. Initially, the input satellite image is de-noised by using the Wavelet Shrinkage de-noising approach. Then the K-Means, Fuzzy C-Means (FCM) and Artificial Bee Colony (ABC) approaches are applied to the de-noised image to segment the vegetation and non-vegetation areas and then extract the features using Local Binary Pattern (LBP) Technique. Finally, the extracted features are given to the KNN, SVM and ELM classifier to get the building map and then the change detection process is applied. In this paper, the comparison is made on three clustering approaches and three classifier approaches to find the best approach for manmade object extraction. From the experimental result, it is shown that the ABC approach performs better than K-Means and FCM in clustering and ELM provides the best result than the KNN and SVM in classifiers.

Building Extraction, Vegetation, Non-Vegetation, Wavelet Shrinkage, FCM, K-Means, ABC, LBP, KNN, SVM, ELM

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