Fusion of Interferometric SAR and Photogrammetric Elevation Data

Neurology & clinical neurophysiolog

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¿ provides on its website several stereo data sets from high and very high resolution spaceborne 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 and -2. The region selected is an area in Catalonia, Spain, including city areas (Barcelona), rural areas and forests in flat and medium undulated terrain as well as steeper mountains. In addition to these data sets, ground truth data: orthoimages from airborne campaigns and Digital Elevation Models (DEM) produced by laser scanning, all data generated by the Institut Cartogràfic 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 and first evaluation results are presented within the paper. Algorithms using area-based least squares matching are compared to those using additionally feature-based matching or newly developed algorithms from the Computer Vision community. 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.JRC.DG.G.2-Global security and crisis managemen

Towards Efficient 3D Reconstructions from High-Resolution Satellite Imagery

Recent years have witnessed the rapid growth of commercial satellite imagery. Compared with other imaging products, such as aerial or streetview imagery, modern satellite images are captured at high resolution and with multiple spectral bands, thus provide unique viewing angles, global coverage, and frequent updates of the Earth surfaces. With automated processing and intelligent analysis algorithms, satellite images can enable global-scale 3D modeling applications. This dissertation explores computer vision algorithms to reconstruct 3D models from satellite images at different levels: geometric, semantic, and parametric reconstructions. However, reconstructing satellite imagery is particularly challenging for the following reasons: 1) Satellite images typically contain an enormous amount of raw pixels. Efficient algorithms are needed to minimize the substantial computational burden. 2) The ground sampling distances of satellite images are comparatively low. Visual entities, such as buildings, appear visually small and cluttered, thus posing difficulties for 3D modeling. 3) Satellite images usually have complex camera models and inaccurate vendor-provided camera calibrations. Rational polynomial coefficients (RPC) camera models, although widely used, need to be appropriately handled to ensure high-quality reconstructions. To obtain geometric reconstructions efficiently, we propose an edge-aware interpolation-based algorithm to obtain 3D point clouds from satellite image pairs. Initial 2D pixel matches are first established and triangulated to compensate the RPC calibration errors. Noisy dense correspondences can then be estimated by interpolating the inlier matches in an edge-aware manner. After refining the correspondence map with a fast bilateral solver, we can obtain dense 3D point clouds via triangulation. Pixel-wise semantic classification results for satellite images are usually noisy due to the negligence of spatial neighborhood information. Thus, we propose to aggregate multiple corresponding observations of the same 3D point to obtain high-quality semantic models. Instead of just leveraging geometric reconstructions to provide such correspondences, we formulate geometric modeling and semantic reasoning in a joint Markov Random Field (MRF) model. Our experiments show that both tasks can benefit from the joint inference. Finally, we propose a novel deep learning based approach to perform single-view parametric reconstructions from satellite imagery. By parametrizing buildings as 3D cuboids, our method simultaneously localizes building instances visible in the image and estimates their corresponding cuboid models. Aerial LiDAR and vectorized GIS maps are utilized as supervision. Our network upsamples CNN features to detect small but cluttered building instances. In addition, we estimate building contours through a separate fully convolutional network to avoid overlapping building cuboids.Doctor of Philosoph

Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics

International audienceIn this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: (1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low level change information between the time layers and object level building description to recognize and separate changed and unaltered buildings. (2) To answering the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. (3) To simultaneously ensure the convergence, optimality and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel non-uniform stochastic object birth process, which generates relevant objects with higher probability based on low-level image features

Assessment of matching algorithms for urban DSM generation from very high resolution satellite stereo images

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Automatic Registration of Optical Aerial Imagery to a LiDAR Point Cloud for Generation of City Models

This paper presents a framework for automatic registration of both the optical and 3D structural information extracted from oblique aerial imagery to a Light Detection and Ranging (LiDAR) point cloud without prior knowledge of an initial alignment. The framework employs a coarse to fine strategy in the estimation of the registration parameters. First, a dense 3D point cloud and the associated relative camera parameters are extracted from the optical aerial imagery using a state-of-the-art 3D reconstruction algorithm. Next, a digital surface model (DSM) is generated from both the LiDAR and the optical imagery-derived point clouds. Coarse registration parameters are then computed from salient features extracted from the LiDAR and optical imagery-derived DSMs. The registration parameters are further refined using the iterative closest point (ICP) algorithm to minimize global error between the registered point clouds. The novelty of the proposed approach is in the computation of salient features from the DSMs, and the selection of matching salient features using geometric invariants coupled with Normalized Cross Correlation (NCC) match validation. The feature extraction and matching process enables the automatic estimation of the coarse registration parameters required for initializing the fine registration process. The registration framework is tested on a simulated scene and aerial datasets acquired in real urban environments. Results demonstrates the robustness of the framework for registering optical and 3D structural information extracted from aerial imagery to a LiDAR point cloud, when co-existing initial registration parameters are unavailable

Reconstruction of 3D scenes from pairs of uncalibrated images. Creation of an interactive system for extracting 3D data points and investigation of automatic techniques for generating dense 3D data maps from pairs of uncalibrated images for remote sensing applications.

Much research effort has been devoted to producing algorithms that contribute directly or indirectly to the extraction of 3D information from a wide variety of types of scenes and conditions of image capture. The research work presented in this thesis is aimed at three distinct applications in this area: interactively extracting 3D points from a pair of uncalibrated images in a flexible way; finding corresponding points automatically in high resolution images, particularly those of archaeological scenes captured from a freely moving light aircraft; and improving a correlation approach to dense disparity mapping leading to 3D surface reconstructions. The fundamental concepts required to describe the principles of stereo vision, the camera models, and the epipolar geometry described by the fundamental matrix are introduced, followed by a detailed literature review of existing methods. An interactive system for viewing a scene via a monochrome or colour anaglyph is presented which allows the user to choose the level of compromise between amount of colour and ghosting perceived by controlling colour saturation, and to choose the depth plane of interest. An improved method of extracting 3D coordinates from disparity values when there is significant error is presented. Interactive methods, while very flexible, require significant effort from the user finding and fusing corresponding points and the thesis continues by presenting several variants of existing scale invariant feature transform methods to automatically find correspondences in uncalibrated high resolution aerial images with improved speed and memory requirements. In addition, a contribution to estimating lens distortion correction by a Levenberg Marquard based method is presented; generating data strings for straight lines which are essential input for estimating lens distortion correction. The remainder of the thesis presents correlation based methods for generating dense disparity maps based on single and multiple image rectifications using sets of automatically found correspondences and demonstrates improvements obtained using the latter method. Some example views of point clouds for 3D surfaces produced from pairs of uncalibrated images using the methods presented in the thesis are included.Al-Baath UniversityThe appendices files and images are not available online

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion