Extraction of spatial information from sterioscopic SAR images

Synthetic Aperture Radar (SAR) is now widely used for generating Digital Elevation Models (DEMs) and has advantages over optical data in terms of availability as it allows all-day and all-weather operations. The stereoscopic SAR method, which allows direct extraction of spatial information in three-dimensional space, has been established for decades. However, the traditional stereoscopic methods developed for SAR data depend on many human operations and need ground control points (GCPs), to set up geometric models. The aims of the thesis are not only to propose a refined rigorous stereoscopic SAR method and a new error model to predict theoretic errors, but also to achieve a higher level of automation and accuracy. By using a weighting matrix, which is derived by considering different observations in the space intersection algorithm, the minimal number of the GCPs required for the refined algorithm is only two. To achieve a high degree of automation, an optimized strategy of parameter selection for the pyramidal image correlation scheme employing a region-growing technique has been proposed. This avoids a trial-and-error approach to produce digital parallax data from the same-side SAR image pairs. A new method to derive GCPs automatically has been developed using a SAR image simulation technique, under the condition that a known DEM chip is available, to minimize human interventions and operator error. The proposed method for providing GCPs and the DEMs generated from space intersection have been incorporated into the procedures for geocoding SAR images to validate the proposed algorithms. The results derived show that the stereoscopic SAR data can be applied to geometric rectification in flat-to-moderate areas, and other applications of extraction of spatial information are promising

On Martian Surface Exploration: Development of Automated 3D Reconstruction and Super-Resolution Restoration Techniques for Mars Orbital Images

Very high spatial resolution imaging and topographic (3D) data play an important role in modern Mars science research and engineering applications. This work describes a set of image processing and machine learning methods to produce the “best possible” high-resolution and high-quality 3D and imaging products from existing Mars orbital imaging datasets. The research work is described in nine chapters of which seven are based on separate published journal papers. These include a) a hybrid photogrammetric processing chain that combines the advantages of different stereo matching algorithms to compute stereo disparity with optimal completeness, fine-scale details, and minimised matching artefacts; b) image and 3D co-registration methods that correct a target image and/or 3D data to a reference image and/or 3D data to achieve robust cross-instrument multi-resolution 3D and image co-alignment; c) a deep learning network and processing chain to estimate pixel-scale surface topography from single-view imagery that outperforms traditional photogrammetric methods in terms of product quality and processing speed; d) a deep learning-based single-image super-resolution restoration (SRR) method to enhance the quality and effective resolution of Mars orbital imagery; e) a subpixel-scale 3D processing system using a combination of photogrammetric 3D reconstruction, SRR, and photoclinometric 3D refinement; and f) an optimised subpixel-scale 3D processing system using coupled deep learning based single-view SRR and deep learning based 3D estimation to derive the best possible (in terms of visual quality, effective resolution, and accuracy) 3D products out of present epoch Mars orbital images. The resultant 3D imaging products from the above listed new developments are qualitatively and quantitatively evaluated either in comparison with products from the official NASA planetary data system (PDS) and/or ESA planetary science archive (PSA) releases, and/or in comparison with products generated with different open-source systems. Examples of the scientific application of these novel 3D imaging products are discussed

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Lunar Terrain and Albedo Reconstruction from Apollo Imagery

Generating accurate three dimensional planetary models and albedo maps is becoming increasingly more important as NASA plans more robotics missions to the Moon in the coming years. This paper describes a novel approach for separation of topography and albedo maps from orbital Lunar images. Our method uses an optimal Bayesian correlator to refine the stereo disparity map and generate a set of accurate digital elevation models (DEM). The albedo maps are obtained using a multi-image formation model that relies on the derived DEMs and the Lunar- Lambert reflectance model. The method is demonstrated on a set of high resolution scanned images from the Apollo era missions

Elevation changes of mountain glaciers in the Antarctic Peninsula using ASTER-controlled archival aerial photography

PhD ThesisOver the last 50 years a significant increase in the atmospheric and upper ocean temperatures in the Antarctic Peninsula (AP) region has been observed. As a result major ice-shelves have retreated during the 20th century. In connection, glaciers have accelerated and an increased dynamic ice mass loss is observed, especially over the last decade. Despite these major changes, an exact quantification of ice mass changes of the AP, with its roughly 1000 glaciers, is not available. Almost no long-term (multi-decadal) glacier mass balance records for the AP exist and in-situ measurements are rare. On the other hand, the United States Geological Survey (USGS) and British Antarctic Survey (BAS) archives hold a large number of historic aerial stereo-photographs of the AP, dating back to the early 1940s. These images contain a valuable source of information and have been used to demonstrate widespread retreat of glaciers in this region. Less effort has been made so far to use this stereo-photography for the extraction of elevation data to compare it with recent elevation information to determine glacier volume change from which mass changes may be estimated. This dissertation seeks to close this research gap and to extend the number of mass balance records for the AP, by investigating, measuring, and analysing historical glacier elevation change in the AP using digital elevation models (DEMs) derived from USGS and BAS airborne (1948-2005) and ASTER spaceborne (2001-2010) stereo imagery. To ensure reliable and accurate measurements of surface elevation change, extracted DEMs need to be registered in a precise manner. The lack of ground control information in the AP is a major obstacle for this and can result in inaccurate absolute orientations of DEMs. If uncorrected, possible offsets between DEMs introduce significant error and i can lead to an over- or underestimation of glacier change. Thus, in order to precisely co-register corresponding historic and modern DEMs an iterative robust least squares surface matching algorithm was applied. The underlying surface matching approach was previously developed for small-scale coastal erosion studies at Newcastle University. Within the context of this work it has been successfully modified and improved to enable large scale glacier change assessment in areas of steep topography which is typical for the AP. For a total of 12 glaciers in the AP, located along the western coast between 64° and 71° S, DEMs from the historic archive stereo-imagery were successfully extracted and combined with DEMs derived from modern aerial and ASTER satellite imagery. The improved surface matching approach allowed precise co-registration of these DEMs and enabled the accurate measurement of glacier surface mass balance at the lower portion of the glaciers. Widespread frontal glacier surface lowering, of up to 50 m, has been observed on 12 glaciers with a mean lowering rate of 0.28 ± 0.03 m/yr over a period of 37 years (1970-2007). Higher rates, of up to 0.6 m/yr, were observed in the north-western Peninsula. Two glaciers which have multi-epoch coverage show a significantly larger-than-average lowering since about 1990. These results are in close correspondence with an increase in positive degree days over the last four decades and suggest that much of this lowering can be attributed to atmospheric forcing. However, the observed spatial and temporal variations in the lowering rates suggests that the pattern of surface change is not a simple one and that a regional upscaling is not straight forward. The glaciers represent only 1.2 % of all estimated glaciers in the AP and only the glacier fronts (~20 % of each glacier) were studied. Observations also show an elevation increase at some higher altitude locations within a few km of the glacier fronts, raising the potential that the lowering may have been at least partially compensated for by increased high-altitude accumulation.British Geological Survey BUFI and NER

Ricerche di Geomatica 2011

Questo volume raccoglie gli articoli che hanno partecipato al Premio AUTeC 2011. Il premio è stato istituito nel 2005. Viene conferito ogni anno ad una tesi di Dottorato giudicata particolarmente significativa sui temi di pertinenza del SSD ICAR/06 (Topografia e Cartografia) nei diversi Dottorati attivi in Italia

Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available

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

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings