An Adaptive Active Contour Model for Building Extraction from Aerial Images

Abstract

Building extraction from aerial images is one of the recent topics of remote sensing that used in many applications such as urban planning, disaster management, military planning, and Geographic Information Systems (GIS). One of the most used approaches in building extraction is the Active Contour Model (ACM) or snakes for its ability to extract contours of structured and unstructured shapes of objects. However, using the traditional ACM snake model in building extraction and other fields faces the problem of extracting contours of concavity regions, because snake points cannot converge inside narrow concavity regions during its movement. In this research we proposed to solve extraction contours of concavity regions problem by adapting coefficients of ACM forces during snake iterations by adding a concavity index to indicate that snake points stop in a concave region or not. Then adapt these coefficients in term of concavity index value, to allow snake converge inside concavity region. Our adaptive model was tested on different sets of sub aerial images of buildings that contain concavity regions, we show the results and evaluate these results using two evaluation methods, the first evaluation method done in terms of accuracy, precision and recall. And the second evaluation method evaluates the Error Distance Ratio ( ) which is the average ratio of distance between each snake point and the true edge map point (by pixels), the result values is compared with the GVF snake model, which is an important improved ACM model that solved the concavity contour extraction problem. In addition, we test and compare the execution time of our adaptive ACM model and GVF model.