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57. GEOMETRIC FEATURES INTERPRETATION OF PHOTOGRAMMETRIC POINT CLOUD FROM UNMANNED AERIAL VEHICLE

Author

Harshit, H., Kushwaha, S. K. P., and Jain, K.

Abstract

Recent days point clouds have become one of the most common 3D sources of information which is provides accurate geometry features of the object. 3D point clouds can be derived from either photogrammetry, Lidar or SAR in some cases depending upon the application. These point clouds consisting of 3D geospatial location of an object in form of XYZ coordinates which can be used in various ways to deduct information related to that object either based on visualisation or geometrical interpretation. Quality assessment standards for these point clouds are still very much in nascent stage with optimum accuracy in relative terms only. In this paper, multiple scale of point cloud has been used to understand the level of information these clouds consist on these multiple scales. Based on the 3D spatial information of these point cloud in local neighbourhood, some of invariant geometric properties can be computed for each 3D point with respective covariance matrix. These can be used to describe the local 3D structure using eigenvalues for these matrices. Using these Geometric features an approach is developed to understand the point cloud quality assessment. The proposed methodology exploits these special geometric properties to evaluate the 3D scene structure. Further, the point cloud is classified using shape detection algorithm which evaluates the geometric features to detect the mathematical shapes in the point cloud. This paper also enlightens on different geometric features that can be extracted from a point cloud and the importance of it.

Published
2022
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78. A large cohort study of TB of the central nervous system: clinical outcomes

Author

Vibha, D., primary, Kushwaha, S., additional, Nath, M., additional, Ramanujam, B., additional, Elavarasi, A., additional, Das, A., additional, Radhakrishnan, D. M., additional, Singh, R. K., additional, Pandit, A. K., additional, Srivastava, A. K., additional, Tripathi, M., additional, Anthony, A., additional, and Maheshwari, S., additional

Published
2022
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80. INTEGRATION OF IPHONE LiDAR WITH QUADCOPTER AND FIXED WING UAV PHOTOGRAMMETRY FOR THE FORESTRY APPLICATIONS.

Author

Yadav, Y., Kushwaha, S. K. P., Mokros, M., Chudá, J., and Pondelík, M.

Subjects
MULTISPECTRAL imaging, OPTICAL radar, LIDAR, FOREST mapping, REMOTE sensing, DIGITAL elevation models
Abstract

The recent innovations in remote sensing technologies have given rise to the efficient mapping and monitoring of forests. The developments in the sensor implementation have mainly focused on optimizing the payload of the UAV system and allowed the users to acquire the data simultaneously with a range of active and passive sensors like high-resolution RGB cameras and multispectral cameras LiDAR (Laser Imaging Detection and Ranging). The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two vegetation seasons, namely leaf-off and leaf-on, were used to combine the Digital Elevation Models from different data acquisition platforms. This internship research work aims to create and experiment with new methods, techniques, and technologies for the applications of UAV photogrammetry and iPhone LiDAR in forest napping and inventory management. CHMs are also generated in this work which helps assess the conditions of the forests in the recreational areas, and the possibility of solutions like iPhone LiDAR and UAV photogrammetry would be highly efficient and economical. The leaf-off and leaf-on datasets were processed in Agisoft Metashape Professional software to generate dense point clouds for the forest plots. The point cloud from the leaf-on dataset was rasterized to generate a DSM whereas the leaf-off point cloud generated a DSM of the forest plots after ground filtering with Cloth Simulation Filter (CSF) plugin. The iPhone LiDAR point was also rasterized to a DTM product after pre-processing steps and noise removal. The Canopy Height Models (CHMs) were generated by subtracting UAV and iPhone LiDAR based DTMs from the UAV leaf on DSM. Finally, the accuracy assessment of CHMs from UAB datasets and their integration with iPhone LiDAR has been assessed using the accurate tree heights measured during the forest field visits. The proposed methodology can be used for forest mapping purposes where a moderate accuracy is requested. [ABSTRACT FROM AUTHOR]

Published
2023
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83. Forest fragmentation in India

Author

Roy, P. S., Murthy, M. S. R., Roy, A., Kushwaha, S. P. S., Singh, S., Jha, C. S., Behera, M. D., Joshi, P. K., Jagannathan, C., Karnatak, H. C., Saran, S., Reddy, C. S., Kushwaha, D., Dutt, C. B. S., Porwal, M. C., Sudhakar, S., Srivastava, V. K., Padalia, Hitendra, Nandy, Subrata, and Gupta, Stutee

Published
2013

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