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ABSTRACT:Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object's surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

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Hyperspectral remote sensing has been widely used for mapping of soil, its classification and also its texture description. It is beneficial in urban and rural management. The present work reports the study regarding classification soil analysis using Support Vector Machine (SVM). Hyperion Hyperspectral satellite data with 10nm fine spectral resolution of Phulambri region of Aurangabad district of Maharashtra (India) which lies between 20° 06' N latitude and 75° 25' E longitude was used for soil classification. Gaussian Radial Basis Function (RBF) kernel of SVM was used to extract five various soils types and achieved overall accuracy of 71.18% and with Kappa Value of 0.57 having sufficient training samples. It was found that the soil of the region may be classified in five categories. The maximum area (51 %) was covered by the brown sandy soil, whereas the minimum (.02%) by gray clay soil. The result is of great significance for soil analysis of very complex region without reduction of dimensionality in satellite data.