Improvement of Degraded Object Shapes Based on Skeletonizing and Recognizing Using Geometry

摘要

Remote sensing technology normally uses for the provision of regional information to support decision making in area utilization, regional boundary determination, infrastructure mapping, etc. Through satellite imagery where it very sensitive to noise causing a shape building objects are degraded and cannot be recognized clearly caused by other objects covering observed building objects and there are holes due to pixel identification errors due to color differences so that the shape of the building object produced is inaccurate and only visible objects in certain parts. Methodology of this research is divided into two stages, the first stage of improvement of degraded building object forms, including separation of buildings with other objects using the K-Means Clustering algorithm (k = 2), Filling Region to cover holes, and Skeletonizing morphology to produce the framework used to search for endpoints that are lost when the indentation leads in, so that in the end the segmentation of the building object line will be formed perfectly through these endpoints; the second stage of the introduction of the shape of the building object using the Harris Corner Detector method to recognize shapes based on the total vertex. The results showed that using Skeletonizing, degraded building objects were able to be refined and can be identified based on angles with relatively high accuracy in 71%.