An object based framework for building change analysis using 2D and 3D information of high resolution satellite images

Abstract

With the rapid change of urban areas in developing countries, construction areas are constantly appearing in different parts of cities. Those changed areas require timely monitoring to provide up-to-date information for of urban information systems. As a result, it is a challenge to develop an effective change analysis of different objects, especially buildings in cities. This paper presents an object-based framework for analyzing building changes from high-resolution satellite stereo images (HRSSI). The disparity information extracted from stereo images and spectral information including visible vegetation index (VVI) help extract the buildings in a hierarchical approach. Evaluations show the accuracy of higher than 98% and F1-Score of higher than 87% for the building extraction step. Then, each building object is classified into three main categories including “Remained Building”, “Removed Building” or “Added Building”. Also, the “Remained Building” objects are categorized into four change states including “Only 2D Change”, “Only 3D Change”, “2D with 3D Change” and “No Change”. This is done by utilizing the object-based similarity analysis of the spectral information as well as the similarity analysis of the disparity information using CNN and their integration. Evaluations demonstrate the accuracy of higher than 97% and F1-Score of higher than 90% for this step.