Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established handcrafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar (SAR), optical images, remote sensing, data fusion, stereogrammetr

Automatic registration of multi-modal airborne imagery

This dissertation presents a novel technique based on Maximization of Mutual Information (MMI) and multi-resolution to design an algorithm for automatic registration of multi-sensor images captured by various airborne cameras. In contrast to conventional methods that extract and employ feature points, MMI-based algorithms utilize the mutual information found between two given images to compute the registration parameters. These, in turn, are then utilized to perform multi-sensor registration for remote sensing images. The results indicate that the proposed algorithms are very effective in registering infrared images taken at three different wavelengths with a high resolution visual image of a given scene. The MMI technique has proven to be very robust with images acquired with the Wild Airborne Sensor Program (WASP) multi-sensor instrument. This dissertation also shows how wavelet based techniques can be used in a multi-resolution analysis framework to significantly increase computational efficiency for images captured at different resolutions. The fundamental result of this thesis is the technique of using features in the images to enhance the robustness, accuracy and speed of MMI registration. This is done by using features to focus MMI on places that are rich in information. The new algorithm smoothly integrates with MMI and avoids any need for feature-matching, and then the applications of such extensions are studied. The first extension is the registration of cartographic maps and image datum, which is very important for map updating and change detection. This is a difficult problem because map features such as roads and buildings may be mis-located and features extracted from images may not correspond to map features. Nonetheless, it is possible to obtain a general global registration of maps and images by applying statistical techniques to map and image features. To solve the map-to-image registration problem this research extends the MMI technique through a focus-of-attention mechanism that forces MMI to utilize correspondences that have a high probability of being information rich. The gradient-based parameter search and exhaustive parameter search methods are also compared. Both qualitative and quantitative analysis are used to assess the registration accuracy. Another difficult application is the fusion of the LIDAR elevation or intensity data with imagery. Such applications are even more challenging when automated registrations algorithms are needed. To improve the registration robustness, a salient area extraction algorithm is developed to overcome the distortion in the airborne and satellite images from different sensors. This extension combines the SIFT and Harris feature detection algorithms with MMI and the Harris corner label map to address difficult multi-modal registration problems through a combination of selection and focus-of-attention mechanisms together with mutual information. This two-step approach overcomes the above problems and provides a good initialization for the final step of the registration process. Experimental results are provided that demonstrate a variety of mapping applications including multi-modal IR imagery, map and image registration and image and LIDAR registration

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

Integration of LiDAR and photogrammetric data for enhanced aerial triangulation and camera calibration

PhD ThesisThe integration of complementary airborne light detection and ranging (LiDAR) and photogrammetric data continues to receive attention from the relevant research communities. Such an approach requires the optimized registration of the two data types within a common coordinate reference frame and thus enables the cross-calibration of one information source against another. This research assumes airborne LiDAR as a reference dataset against which in-flight camera system calibration and validation can be performed. The novel methodology involves the production of dense photogrammetric point clouds derived using the simultaneous adjustment of GNSS/IMU data and a dense set of photogrammetric tie points. Quality of the generated photogrammetric dataset is further improved through introducing the self-calibration additional parameters in the combined adjustment. A robust least squares surface matching algorithm is then used to minimise the Euclidean distances between the two datasets. After successful matching, well distributed LiDAR-derived control points (LCPs) are automatically identified and extracted. Adjustment of the photogrammetric data is then repeated using extracted LCPs in a self-calibrating bundle adjustment. The research methodology was tested using two datasets acquired using different photogrammetric digital sensor systems, a Microsoft UltraCamX large format camera and an Applanix DSS322 medium format camera. Systematic sensitivity testing included the influence of the number and weighting of LCPs required to achieve optimised adjustment. For the UltraCamX block it was found that when the number of control points exceeded 80, the accuracy of the adjustment stabilized at c. 2 cm in all axes, regardless of point weighting. Results were also compared with those from reference calibration using surveyed ground control points in the test area, with good agreement found between the two. Similar results were obtained for the DSS322 block, with block accuracy stabilizing at 100 LCPs. Moreover, for the DSS322 camera, introducing self-calibration greatly improved the accuracy of aerial triangulation

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection

Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes

There is much current interest in using multisensor airborne remote sensing to monitor the structure and biodiversity of woodlands. This paper addresses the application of nonparametric (NP) image-registration techniques to precisely align images obtained from multisensor imaging, which is critical for the successful identification of individual trees using object recognition approaches. NP image registration, in particular, the technique of optimizing an objective function, containing similarity and regularization terms, provides a flexible approach for image registration. Here, we develop a NP registration approach, in which a normalized gradient field is used to quantify similarity, and curvature is used for regularization (NGF-Curv method). Using a survey of woodlands in southern Spain as an example, we show that NGF-Curv can be successful at fusing data sets when there is little prior knowledge about how the data sets are interrelated (i.e., in the absence of ground control points). The validity of NGF-Curv in airborne remote sensing is demonstrated by a series of experiments. We show that NGF-Curv is capable of aligning images precisely, making it a valuable component of algorithms designed to identify objects, such as trees, within multisensor data sets.This work was supported by the Airborne Research and Survey Facility of the U.K.’s Natural Environment Research Council (NERC) for collecting and preprocessing the data used in this research project [EU11/03/100], and by the grants supported from King Abdullah University of Science Technology and Wellcome Trust (BBSRC). D. Coomes was supported by a grant from NERC (NE/K016377/1) and funding from DEFRA and the BBSRC to develop methods for monitoring ash dieback from aircraft.This is the final version. It was first published by IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7116541&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_Publication_Number%3A36%29%26pageNumber%3D5

Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper