• Ecology and Resilient Infrastructure
• /
• v.8 no.4
• /
• pp.266-272
• /
• 2021

Water friendly space refers to a place designated to plan and manage spaces for residents Water friendly activities. Efficient management of river Water friendly parks requires automated GIS data and DB construction of the water friendly facilities. Object-based classification using drone images or aerial images is attracting attention as an efficient means to acquire 3D spatial information in the country. To remove the miscellaneous image included in the extracted outline, a linear simplification of the outline is required, and it is difficult to apply manually, so various automation methods have been developed to overcome this, and among them, the most widely studied and utilized is the linear simplification method. In this study, the suitability of linear simplification algorithms such as Douglas-Peucker, Visvalingam-Whyatt, and Bend-simplify algorithms for the geometric shape of hydrophilic facilities was determined.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.45 no.9
• /
• pp.794-806
• /
• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.