Pablo d'Angelo

Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area Urban Mass concentrations

Building models are a valuable information source for urban studies and in particular for analyses of urban mass concentrations (UMCS). Most commonly, light detection and ranging (LiDAR) is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, comparably high costs and low actualization rates. Spaceborne stereo data from Cartosat-1 are able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper, we investigate to which extent the geometric shortcomings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models (DSMs) from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25 000 (DTK25). Both data are fused to generate building block models for four metropolitan regions in Germany with an area of ~ 16 000 km2. Building block models are further aggregated to 1 × 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of UMCs. Performance evaluation of the building block models reveals that the building footprints are larger in the DTK-25, and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building volume, which is linearly lower than the reference. However, this error does not affect the classification of UMC, which can be classified with accuracies between 77% and 97%.

In-flight Geometric Calibration and Orientation of ALOS/PRISM Imagery with a Generic Sensor Model

Self-calibration is a powerful technique to exploit the geometric potential of optical spaceborne sensors. This paper explains the methodology of expanding a sensor model for in-orbit geometric calibration of the PRISM radiometers on the Japanese ALOS satellite. PRISM has three optical systems for forward, nadir, and backward views each with a 2.5 m nominal spatial resolution. Algorithms for the geometric processing of the PRISM images are proposed and implemented. It is shown how self calibration and orientation of the sensor can be done without having precise knowledge of the payload geometry and attitude data. Several cases and procedures are studied with the established sensor model, including weight matrices, attitude offsets, attitude drifts, and focal length estimations. It is concluded that self calibration of the PRISM cameras can be done effectively with a rigorous sensor model. Even if the post-launch parameters are not available, sub-pixel geometric accuracy can be achieved.

Benchmarking and quality analysis of DEM generated from high and very high resolution optical stereo satellite data

The Working Group 4 of Commission I on “Geometric and Radiometric Modelling of Optical Spaceborne Sensors” will provide on its website several stereo data sets from high and very high resolution spaceborne stereo sensors. Among these are data from the 2.5 meter class like ALOS-PRISM and Cartosat-1 as well as, in near future, data from the highest resolution sensors (0.5 m class) like GeoEye-1 and Worldview-1. The region selected is an area in Catalonia, Spain, including city areas (Terrassa), rural areas and forests in flat and medium undulated terrain as well as steep mountainous terrain. In addition to these data sets, ground truth data like orthoimages from airborne campaigns and Digital Elevation Models (DEM) produced by laser scanning, all data generated by the Institut Cartografic de Catalunya (ICC), are provided as reference for comparison. The goal is to give interested scientists of the ISPRS community the opportunity to test their algorithms on DEM generation, to see how they match with the reference data and to compare their results within the scientific community. A second goal is to develop further methodology for a common DEM quality analysis with qualitative and quantitative measures. Several proposals exist already and the working group is going to publish them on their website. But still there is a need for more standardized methodologies to quantify the quality even in cases where no better reference is available. The data sets, the goal of the benchmarking, the comparison strategy and first very preliminary evaluation results with some data of the selected areas are presented within the paper. The main goal though is to motivate further researchers to join the benchmarking and to discuss pros and cons of the methods as well as to trigger the process of establishing standardized DEM quality figures and procedures.

Iterative approach for efficient digital terrain model production from CARTOSAT-1 stereo images

This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution CARTOSAT-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas.

Comparison of dense stereo using CUDA

In this paper, a local and a global dense stereo matching method, implemented using Compute Unified Device Architecture (CUDA), are presented, analyzed and compared. The purposed work shows the general strategy of the parallelization of matching methods on GPUs and the tradeoff between accuracy and run-time on current GPU hardware. Two representative and widely-used methods, the Sum of Absolute Differences (SAD) method and the Semi-Global Matching (SGM) method, are used and their results are compared using the Middlebury test sets.

Dense multi-view stereo from satellite imagery

Digital surface models can be efficiently generated with automatic image matching from optical stereo images. Modern satellites, such as WorldView-1 and 2 can acquire multiple views of an area in the same orbit. These datasets offer high redundancy and thus allow higher quality 3D reconstructions than previously possible. This paper evaluates multiple DSM generation algorithms based on dense image matching on a 25 view dataset acquired by the WorldView-2 satellite. Fusion of the images is performed at different stages of image matching and results are compared to reveal the advantages and disadvantages of each method.

Automatic urban area monitoring using digital surface models and shape features

Accurate monitoring of urban areas using remote sensing data requires reliable change detection techniques. Nevertheless, while most of the changes are optically visible and easily detectable by an expert user, automatic processes are quite difficult to develop. That is why, the interpretation of changes has remained up-to-now visual in most operational applications in remote sensing. This paper provides an automatic approach for 3D change detection based on the joint use of the height and spatial information. In fact, when dealing with urban areas, one possibility to cope with the automatic growth monitoring is the exploitation of the height information relative to the different man-made objects that exist in the scene. The subtraction of Digital Surface Models (DSMs), acquired at different epochs, should thus provide a valuable information about the 3D urban changes occurred in the studied area. However, when at least one of the DSMs presents some artifacts, a simple DSM subtraction could result also in the detection of virtual changes. To remove these virtual changes, we propose in this work to include, in addition to the height information, some shape features that could be of a great help in describing the geometry of the constructed or demolished man-made structures. After that, the Support Vector Machine (SVM) classifier is used to differentiate real from virtual changes. Evaluation of the proposed approach in terms of completeness, correctness, overall accuracy, etc has been performed proving its efficiency and relatively high accuracy.

Spatially Regularized Fusion of Multiresolution Digital Surface Models

In this paper, we propose an algorithm for robustly fusing digital surface models (DSMs) with different ground sampling distances and confidences, using explicit surface priors to obtain locally smooth surface models. Robust fusion of the DSMs is achieved by minimizing the L1-distance of each pixel of the solution to each input DSM. This approach is similar to a pixel-wise median, and most outliers are discarded. We further incorporate local planarity assumption as an additional constraint to the optimization problem, thus reducing the noise compared with pixel-wise approaches. The optimization is also inherently able to include weights for the input data, therefore allowing to easily integrate invalid areas, fuse multiresolution DSMs, and to weight the input data. The complete optimization problem is constructed as a variational optimization problem with a convex energy functional, such that the solution is guaranteed to converge toward the global energy minimum. An efficient solver is presented to solve the optimization in reasonable time, e.g., running in real time on standard computer vision camera images. The accuracy of the algorithms and the quality of the resulting fused surface models are evaluated using synthetic data sets and spaceborne data sets from different optical satellite sensors.

The TUM-DLR Multimodal Earth Observation Evaluation Benchmark

We present a new dataset for development, benchmarking, and evaluation of remote sensing and earth observation approaches with special focus on converging perspectives. In order to provide data with different modalities, we observed the same scene using satellites, airplanes, unmanned aerial vehicles (UAV), and smartphones. The dataset is further complemented by ground-truth information and baseline results for different application scenarios. The provided data can be freely used by anybody interested in remote sensing and earth observation and will be continuously augmented and updated.

A performance study on different stereo matching costs using airborne image sequences and satellite images

Most recent stereo algorithms are designed to perform well on close range stereo datasets with relatively small baselines and good radiometric conditions. In this paper, different matching costs on the Semi-Global Matching algorithm are evaluated and compared using aerial image sequences and satellite images with ground truth. The influence of various cost functions on the stereo matching performance using datasets with different baseline lengths and natural radiometric changes is evaluated. A novel matching cost merging Mutual Information and Census is introduced and shows the highest robustness and accuracy. Our study indicates that using an adaptively weighted combination of Mutual Information and Census as matching cost can improve the peformance of stereo matching for airborne image sequences and satellite images.