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

Abstract

Abstract:A successful application of dense image matching algorithms to historical aerial photographs would offer a great potential for detailed reconstructions of historical landscapes in three dimensions, allowing for the efficient monitoring of various landscape changes over the last 50+ years. In this paper we propose the combination of image-based dense DSM (digital surface model) reconstruction from historical aerial imagery with object-based image analysis for the detection of individual buildings and the subsequent analysis of settlement change. Our proposed methodology is evaluated using historical greyscale and color aerial photographs and numerous reference data sets of Andermatt, a historical town and tourism destination in the Swiss Alps. In our paper, we first investigate the DSM generation performance of different sparse and dense image matching algorithms. They demonstrate the superiority of dense matching algorithms and of the resulting historical DSMs with root mean square error values of 1-1.5 GSD (ground sampling distance) and yield point densities comparable to those of recent airborne LiDAR DSMs. In the second part, we present an object-based building detection workflow mainly based on the historical DSMs and the historical imagery itself. Additional inputs are a current digital terrain model and a cadastral building database. For the case of densely matched DSMs, the evaluation yields building detection rates of 92% for grayscale and 94% for color imagery.

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

“…Based on these characteristics, it is promising to analyze the building primitives in SAR imagery by combining OBIA method and the semantic knowledge. However, because of the heterogeneity of the urban landscape and its spatial variability, the segmentation of urban scenes is a challenging task [31,52], especially in SAR images, which are affected by speckle inevitably. In this subsection, we first introduce an object-based segmentation for SAR images.…”

“…Object-Based Image Analysis (OBIA) is proposed as a sub-discipline of GIScience devoted to partitioning remote sensing imagery into meaningful image objects and assessing their characteristics through spatial, spectral and temporal scale [31,51]. OBIA claims to overcome problems of traditional pixel-based techniques of high spatial resolution image data, by defining segments rather than pixels to classify, and allowing spectral reflectance variability to be used as an attribute for discriminating features in the segmentation approach [22,30].…”

Exploiting the Redundancy of Multiple Overlapping Aerial Images for Dense Image Matching Based Digital Surface Model Generation

“…Objects in the side-lap areas are visible on 10 images. According to the binomial coefficient presented in Equation (1), where n is the number of overlapping images (10) and k is the number of images forming a stereo pair (2), it can be derived that 45 stereo combinations can be established for each part of the terrain in the side-lap area.…”

“…It provides a valuable source of information about the environment which can be applied in many areas of human activity such as change detection in rural landscape [1,2], solar radiation modeling [3][4][5], change detection in urban areas [6,7], urban planning [8], GNSS multipath prediction [9], sound propagation modeling [10], wind flow simulation [11], power-line corridors management [12] or visibility analysis [13]. The crucial issue for the application of the DSM is its reliability.…”

Photogrammetric Modelling for Urban Medieval Site Mapping. A Case Study from Curtea de Argeş, Romania

“…Several scientific papers focused on the importance of historical sites, most of them having a chronological significance in the context of urban development along centuries (Rodrigues et al 2011, Del Lama et al 2015, Erikstad et al 2017. Historical aerial images have a great contribution for monitoring topography changes due to anthropic factors (Scardozzi 2010, Doering et al 2012, for tracking land cover changes (Ruan, Ellis 2004, Jao et al 2014, for archaeological site studies (Stichelbaut 2006) and for monitoring urban sprawl (Dadras et al 2014, Nebiker et al 2014, Mihai et al 2016.…”