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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Remote sensing is an efficient technology and worthy source of earth surface information, as it can capture images of reasonably large area on the earth. Due to advancement in the sensor technologies there is availability of high spatial as well as spectral resolutions imageries, and also non imaging Spectroradiometer. With the use of these imaging and non-imaging data we can easily characterize the different species. In this article we have reported work done by worldwide researchers for spatial as well as spectral feature extraction from remote sensing data; specifically we have focused on classification of crops and use narrow band vegetation indices. It may be observed from the report that both spatial resolution and hyperspectral imageries need to be used for better classification.

The present study demonstrates the application of remote sensing for the estimation of areas corresponding to crop and forest lands covered in the district of Aurangabad, (Maharashtra), India. The data acquired by IRS-P6 Advanced Wide Field Sensors (AWiFS) having 56m spatial resolution for the months of October & December 2012 which covered complete study areas with its swath of 740Km has been used for the study. The Maximum Likelihood Classification (MLC) and Knowledge Classification (KC) techniques based on Decision Tree approach were applied. It has basically two elements, knowledge engineering and knowledge classifier. Knowledge engineering provides an interface to build up decision tree which defines the rules and variables represented by three parameters, i.e. Normalized Difference Vegetation Index (NDVI), Soil Adjust Vegetation Index (SAVI), and Normalized Difference Water Index (NDWI) threshold value of each class. Knowledge classifier generates the required output classification. The objective of this research work is to perform classification of crop and forest acreage estimation from the AWiFS data and comparing it to the supervised classification techniques, MLC and KC. The result shows that values of overall classification accuracy were 82% and 84% for the months of December and October 2012 respectively using MLC, whereas corresponding values of accuracy were found to be 85% and 87% based on KC. Thus the classification results based on KC provide better results than corresponding results based on the MLC.

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