Multitemporal 3D Change Detection in Urban Areas Using Stereo Information from Different Sensors

Abstract

For a detailed monitoring of cities only very high resolution (VHR) satellite images as well as airborne imagery are able to provide the required accuracy. Especially not well mapped cities and new build up areas and their infrastructure like buildings, streets, bridges and other objects are of interest. Just the 2D information delivered by single images is often not sufficient when dealing with changes. Therefore stereo imagery is of importance to derive the height component which is of high value in analyzing 3D objects. The paper describes 3D change detection methodology if several Digital Surface Models (DSM) from different dates and sensors are available and provides solutions by the joint use of height and spectral information. The subtraction of DSMs, computed from image pairs acquired at different dates, should give direct results about changes. However, when at least one of the DSMs presents artifacts, a simple DSM subtraction often results in the detection of a combination of real and virtual changes. Therefore morphological based post-processing steps are proposed and adapted to different DSM qualities in order to remove artificial changes. In addition to height information, vegetation and shadow masks are generated, which are of great help in reconstructing sharper and more correct building boundaries. The used data are acquired over the city of Munich, Germany. They consist of a time series of stereo images acquired within the years 2005 to 2010 from space borne (IKONOS and Worldview) and airborne (3K-Camera-System) sensors. Evaluation of the proposed approaches in terms of false alarm, missed alarm and Kappa Coefficient etc is performed, proving its efficiency and relatively high accuracy for different kinds and combinations of stereo images and consequently different DSM qualities.

Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis

“…Further work using DSMs from either LiDAR, airborne, and spaceborne digital sensors with OBIA for urban and building change detection include [7,[19][20][21][22]. All of these approaches exploit the multispectral characteristics of digital imaging sensors in order to reliably separate vegetation from the process, typically by means of the NDVI vegetation index [7,19].…”

3D-information fusion from very high resolution satellite sensors

“…An additional method used here is the shadow detection shown by Makarau et al (2011). The basic approach for change detection used in this work is the robust change detection method described in Tian and Reinartz (2011) and Tian et al (2014).…”

“…The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11-15 May 2015, Berlin, Germany In addition to the height classification also a spectral classification is needed for detection of urban objects or the change detection methods like the one described in Tian and Reinartz (2011). For this the multispectral imagery is converted first to a top of atmosphere (TOA) reflectances image Itoa.…”

A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area

“…Fusion of multi-sensor data provides complementary data from the same observed site results in a superior comprehension of the scene which is impossible with single sensor data (Bigdeli, Samadzadegan, & Reinartz, 2014;Du, Liu, Xia, & Zhao, 2013;Lu et al, 2015). In this regard, image analysis and data fusion play several roles in image pansharpening (Guo, Zhang, Li, Zhang, & Shen, 2014;Thomas, Ranchin, Wald, & Chanussot, 2008;Wald, 1999), classification (Camps-Valls, Tuia, Bruzzone, & Benediktsson, 2014;Fauvel, Tarabalka, Benediktsson, Chanussot, & Tilton, 2013;Plaza et al, 2009;Huang & Zhang, 2012a), change detection (Bruzzone & Bovolo, 2013;Huang, Zhang, & Zhu, 2014;Tian & Reinartz, 2011), large-scale processing (Blanchart, Ferecatu, Cui, & Datcu, 2014;Espinoza-Molina & Datcu, 2013), multiple resolution (Voisin, Krylov, Moser, Serpico, & Zerubia, 2014;Wemmert, Puissant, Forestier, & Gancarski, 2009), domain adaption (Bruzzone & Marconcini, 2009;Persello & Bruzzone, 2012;Tuia, Volpi, Trolliet, & Camps-Valls, 2014), interactive systems (Crawford, Tuia, & Yang, 2013;Tuia, Volpi, Copa, Kanevski, & Munoz-Mari, 2011) and signal modalities fusion with different meaning and features (Moser, Serpico, & Benediktsson, 2013). Among the mentioned fusion tasks, urban area classification is one of the most challenging issues due to land-cover kinds' complexity and diversity of man-made objects and therefore attracts a lot of research interests (Lu et al, 2015;.…”