MODIS. Volume 2: MODIS level 1 geolocation, characterization and calibration algorithm theoretical basis document, version 1

The EOS Moderate Resolution Imaging Spectrometer (MODIS) is being developed by NASA for flight on the Earth Observing System (EOS) series of satellites, the first of which (EOS-AM-1) is scheduled for launch in 1998. This document describes the algorithms and their theoretical basis for the MODIS Level 1B characterization, calibration, and geolocation algorithms which must produce radiometrically, spectrally, and spatially calibrated data with sufficient accuracy so that Global change research programs can detect minute changes in biogeophysical parameters. The document first describes the geolocation algorithm which determines geodetic latitude, longitude, and elevation of each MODIS pixel and the determination of geometric parameters for each observation (satellite zenith angle, satellite azimuth, range to the satellite, solar zenith angle, and solar azimuth). Next, the utilization of the MODIS onboard calibration sources, which consist of the Spectroradiometric Calibration Assembly (SRCA), Solar Diffuser (SD), Solar Diffuser Stability Monitor (SDSM), and the Blackbody (BB), is treated. Characterization of these sources and integration of measurements into the calibration process is described. Finally, the use of external sources, including the Moon, instrumented sites on the Earth (called vicarious calibration), and unsupervised normalization sites having invariant reflectance and emissive properties is treated. Finally, algorithms for generating utility masks needed for scene-based calibration are discussed. Eight appendices are provided, covering instrument design and additional algorithm details

Coordinates and maps of the Apollo 17 landing site

We carried out an extensive cartographic analysis of the Apollo 17 landing site and determined and mapped positions of the astronauts, their equipment, and lunar landmarks with accuracies of better than ±1 m in most cases. To determine coordinates in a lunar body‐fixed coordinate frame, we applied least squares (2‐D) network adjustments to angular measurements made in astronaut imagery (Hasselblad frames). The measured angular networks were accurately tied to lunar landmarks provided by a 0.5 m/pixel, controlled Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) orthomosaic of the entire Taurus‐Littrow Valley. Furthermore, by applying triangulation on measurements made in Hasselblad frames providing stereo views, we were able to relate individual instruments of the Apollo Lunar Surface Experiment Package (ALSEP) to specific features captured in LROC imagery and, also, to determine coordinates of astronaut equipment or other surface features not captured in the orbital images, for example, the deployed geophones and Explosive Packages (EPs) of the Lunar Seismic Profiling Experiment (LSPE) or the Lunar Roving Vehicle (LRV) at major sampling stops. Our results were integrated into a new LROC NAC‐based Apollo 17 Traverse Map and also used to generate a series of large‐scale maps of all nine traverse stations and of the ALSEP area. In addition, we provide crater measurements, profiles of the navigated traverse paths, and improved ranges of the sources and receivers of the active seismic experiment LSPE

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

Understanding land administration systems

This is a preprint of a paper from 14th PCGIAP Meeting (International Seminar on Land Administration Trends & Issues in Asia & The Pacific Region), 19-20 August 2008. http://www.csdila.unimelb.edu.au/projects/PCGIAPLASeminar/index.html.19-20 August 200

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains

The Segmentation of Reflectances from Moderate Resolution Remote Sensing Data for the Retrieval of Land Cover Specific Leaf Area Index

A method is developed to incorporate prior fuzzy knowledge about reflectance behavior of land cover types into the segmentation of reflectances from moderate scale remote sensing data. The procedure is applied to aggregated Landsat TM data and to MODIS data and used to derive land cover type specific leaf area index

Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery taken from the overhead perspective and terrestrial imagery are discussed, as well as difficulties in using geospatial overhead imagery for image registration due to a lack of suitable machine learning datasets. Geospatial analysis is conducted to identify suitable areas for future UAS imagery collection. One of these areas, Jerusalem northeast (JNE) is selected as the area of interest (AOI) for this research. Multi-modal, multi-temporal, and multi-resolution geospatial overhead imagery is aggregated from a variety of publicly available sources and processed to create a controlled image dataset called Jerusalem northeast rural controlled imagery (JNE RCI). JNE RCI is tested with handcrafted feature-based methods SURF and SIFT and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the coarse-grained registration process with DLCNNs and the process to create a new supervised machine learning dataset, Jerusalem northeast machine learning (JNE ML) is covered in detail. A multi-resolution grid based approach is used, where each grid cell ID is treated as the supervised training label for that respective resolution. Pre-trained fine-tuned VGG-16 DLCNNs, two custom architecture two-channel DLCNNs, and a custom chain DLCNN are trained on JNE ML for each spatial resolution of subimages in the dataset. All DLCNNs used could more accurately coarsely register the JNE ML subimages compared to the pre-trained fine-tuned VGG-16 DLCNN on JNE RCI. This shows the process for creating JNE ML is valid and is suitable for using machine learning with the coarse-grained registration problem. All custom architecture two-channel DLCNNs and the custom chain DLCNN were able to more accurately coarsely register the JNE ML subimages compared to the fine-tuned pre-trained VGG-16 approach. Both the two-channel custom DLCNNs and the chain DLCNN were able to generalize well to new imagery that these networks had not previously trained on. Through the contributions of this research, a foundation is laid for future work to be conducted on the UAS global localization problem within the rural forested JNE AOI

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Verbesserte Dokumentation des kulturellen Erbes mithilfe digitaler Photogrammetrie mit sichtbaren und thermischen Bildern von unbemannten Luftfahrzeugen (UAV)

There is always need for reliable and accurate data for documentation of cultural heritage including archaeological areas. The development in 3D data acquisition has let some technologies use for getting a complete documentation. Close range photogrammetry and terrestrial laser scanning are among the most common used techniques which help to get 3D data acquisition, with high level of detail, accuracy and effective results. However, these techniques are not always the most suitable ones for large archaeological areas, yet aerial images may help to provide a general overview of the area which is fundamental for interpretation and documentation of archaeological sites. Because of the limitations in aerial photogrammetry, UAVs (Unmanned Aerial Vehicles) has become an optimal solution for archaeological areas documentation with its potentials in the context of costs and abilities. To cover large areas at different altitudes, to be able to fly at different altitudes, under different weather conditions, to acquire image with high resolution are among the main advantages of this technology which make it usable and preferable for archaeological documentation. Since UAVs have been rapidly improving in sophistication and reliability, its possibilities aid in archaeological research have recently generated much interest, particularly for documenting sites, monuments and excavations. In this case study several aerial surveys will be conducted with a UAV mounted thermal camera on an archaeological area. After acquiring aerial images, they will be processed for producing both color and thermal-imagery in related software. Next step will be the alignment of the images in order to build an accurate and georeferenced 3D and mesh model of surveyed area. Then colored and thermal orthophoto mosaics as well as digital surface model (DSM) will be obtained for the documentation. The datasets of thermal images and color images will be collected and compared in order to detect archaeological remains on and under the ground.Es besteht immer Bedarf an zuverlässigen und genauen Daten für die Dokumentation des kulturellen Erbes, einschließlich archäologischer Gebiete. Die technischen Entwicklungen in der 3D-Datenerfassung haben erst die vollständige Dokumentation ermöglicht. Nahbereichsphotogrammetrie und terrestrisches Laserscanning gehören zu den am häufigsten verwendeten Techniken, die 3D-Datenerfassung mit hohem Detaillierungsgrad, Genauigkeit und effektive Ergebnissen ermöglichen. Diese Techniken sind jedoch nicht immer die am besten geeigneten für große archäologische Gebiete, dennoch können Luftbilder helfen, einen allgemeinen Überblick über das Gebiet zu geben, was für die Interpretation und Dokumentation archäologischer Stätten von grundlegender Bedeutung ist. Aufgrund der Einschränkungen in der Luftbildvermessung sind UAVs (Unmanned Aerial Vehicles) zu einer optimalen Lösung für die archäologische Geländedokumentation mit ihren Potenzialen im Kontext von Kosten und Fähigkeiten geworden. Hauptvorteile dieser Technologie sind u.a. große Gebiete in verschiedenen Höhen abzudecken und unter verschiedenen Wetterbedingungen fliegen zu können, Bilder mit hoher Auflösung aufzunehmen, die dann auch für die archäologische Dokumentation nutzbar und damit auch anderen Verfahren vorzuziehen sind. Da sich die UAVs in Bezug auf Entwicklungsgrad und Zuverlässigkeit rasant verbessert haben, haben ihre Möglichkeiten zur Unterstützung der archäologischen Forschung in jüngster Zeit großes Interesse geweckt, insbesondere bei der Dokumentation von Stätten, Denkmälern und Ausgrabungen. In dieser Fallstudie werden mehrere Kampagnen von Luftaufnahmen mit einer UAV-Wärmebildkamera auf einem archäologischen Gebiet durchgeführt. Nach der Bildaufufnahme die Farb- und Wärmebilder in einer entsprechenden Software verarbeitet. Der nächste Schritt wird die Verknüpfung der Bilder sein, um ein genaues und georeferenziertes 3D- und Netzmodell des vermessenden Bereichs zu erstellen. Anschließend werden farbige und thermische Orthophoto-Mosaike sowie digitale Oberflächenmodelle (DSM) für die Dokumentation abgeleitet. Die Datensätze von Wärme- und Farbbildern werden gesammelt und verglichen, um archäologische Überreste auf und unter dem Boden zu erkennen

United States Air Force Applications of Unmanned Aerial Systems: Modernizing Airfield Damage Assessment

Modernizing airfield damage assessment has long been a priority mission at the Air Force Civil Engineer Center (AFCEC). Previously, AFCEC has made advances to expedite unexploded ordnance (UXO) neutralization and pavement repair. Missing from these initiatives is the initial assessment component. This thesis expands the idea of using Small Unmanned Aerial Systems (SUAS), applies it to the Air Force mission, and provides SUAS vehicle configuration and sensor recommendations. In this study, 25 civil engineer officers reviewed airfield imagery gathered using two small air vehicles. For the first review, participants attempted to identify UXOs and foreign object debris (FOD) in a computer interface that leverages images collected by a fixed-wing air vehicle. The second review uses a two-dimensional map created using a hex-rotor. The results of both systems were then compared to the status quo. Resulting statistics indicate that, irrespective of image resolution, additional analysis time does not result in greater object detection or correct identification. Overall, this thesis concludes that SUAS use for afield damage assessment shows promise. Moreover, they can provide the Air Force improved precision for locating UXOs and FOD, as well as estimate dimensions of damage. Dedicating resources to developing this technology will also assist with improving object detection and manpower efficiency. Further research is required for optimal image characterization requisite for reducing and/or eliminating the occurrence of false negative events