Terrain Extraction in Built-Up Areas from Satellite Stereo-Imagery-Derived Surface Models: A Stratified Object-Based Approach

Fritjof Luethje,Dirk Tiede,Clemens Eisank

doi:10.3390/ijgi6010009

Fritjof Luethje, Dirk Tiede + Show 1 more

Open Access

https://doi.org/10.3390/ijgi6010009

Copy DOI

Abstract

Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observed—especially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM and—in accordance with the RUI—differs between the different land cover classes.

Highlights

Very high spatial resolution (VHSR) stereo-imagery from airborne and space-borne sensor systems enable the generation of digital surface models (DSM) using digital photogrammetric approaches [1,2,3]
For the analysis of natural features and processes such as landslides [4,5], landforms [6], or man-made structures, digital elevation model (DEM) are preferred over DSMs, because physically-based models need to work with bare ground data [7]. normalized DSM (nDSM) are especially important in urban areas to serve purposes such as the derivation of the 3D properties of urban buildings, which represent the three-dimensional nature of living spaces and are needed in population estimation or urban planning [8,9,10]
Since the stereo DSM is based on photogrammetric methods, the edges of elevated objects are smoother and remain visible when subtracting the Light Detection and Ranging (LiDAR) DSM from the stereo DSM [27]

Summary

Introduction

Very high spatial resolution (VHSR) stereo-imagery from airborne (resolution of a few centimeters) and space-borne (resolution of a few meters to sub-meter) sensor systems enable the generation of digital surface models (DSM) using digital photogrammetric approaches [1,2,3]. Continuous optimization and improvement of the aforementioned approaches are achieved through statistical learning methods such as artificial neural networks or genetic algorithms [16], adaptive filtering methods [17], locally fitted surfaces within the data [18], volume-based filtering methods [19], slope-dependent filtering [20], and multi-directional slope-dependent filtering [21] or slope-based region-building and subsequent interpolation [22,23] All of these techniques require the adjustment of a maximum filter size, a certain threshold value, or at least a high degree of awareness of the DEM application purpose [24]. These approaches need a high degree of user–algorithm interaction, and the user should be experienced in producing DEMs

Methods

Results

Conclusion