Analysis of the quality of image data acquired by the LANDSAT-4 thematic mapper and multispectral scanners

Image products and numeric data were extracted from both TM and MSS data in an effort to evaluate the quality of these data for interpreting major agricultural resources and conditions in California's Central Valley. The utility of TM data appears excellent for meeting most of the inventory objectives of the agricultural resource specialist. These data should be extremely valuable for crop type and area proportion estimation, for updating agricultural land use survey maps at 1:24,000-scale and smaller, for field boundary definition, and for determining the size and location of individual farmsteads

Interpreting Vegetation and Soil Anomalies in the Guarumen Area of Northwestern Venezuela Using Remote Sensing Applications

The Guarumen area of Venezuela is a tectonically active region that is approximately 1,640 mi2 across the northern portions of the Barinas Basin and the foothills of the Mérida Andes. It is structurally influenced by the Caribbean plate to the north, the Nazca plate to the west, and the Maracaibo block against the Guyana Shield of the South American Plate. These result in an oblique boundary that gives rise to the fold-and-thrust belt of the Mérida Andes to the west, and the Caribbean Mountain system to the north, in concordance to the right-lateral shearing that is evidenced by the Boconó fault system. The goal of this research was to investigate the geological setting of northwestern Venezuela and further understand the geologic controls of the region, as it has become a region of interest for mineral, oil, and gas exploration. To achieve the goal, hyperspectral and multispectral data analysis were used to address land cover types by reducing hyperspectral and multispectral spectra to unique endmembers for use in classification. Then, provide an accurate land cover analysis using derived endmembers to characterize the outcomes concerning the influence of geological phenomena, and determine if microclimate analysis using satellite-based land surface temperature data can be effectively used to infer geologic structure or geomorphology, particularly soils and vegetation. Based on the hyperspectral data, an in-depth endmember analysis was conducted with image-derived spectra. These spectra were plotted in comparison with spectral libraries to identify the anomaly classification. It was determined that the natural vegetation make up of a specific region helped identify soil type. The Guarumen area was influenced by the sediment transport of the alluvial stream geomorphology of both the Merida Andes and the Caribbean Mountain System and both its respective geologies. The microclimate analysis shoa land surface temperature comparison of two separate Landscenes. Both shoa similar mean temperature range due to Venezuela’s tropical climate, but differed in other classifications. Results from this research show that remote sensing applications with limited field data can provide accurate land cover analysis concerning geological phenomena, but further field analysis is needed for more detailed classification

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

Image Understanding at the GRASP Laboratory

Research in the GRASP Laboratory has two main themes, parameterized multi-dimensional segmentation and robust decision making under uncertainty. The multi-dimensional approach interweaves segmentation with representation. The data is explained as a best fit in view of parametric primitives. These primitives are based on physical and geometric properties of objects and are limited in number. We use primitives at the volumetric level, the surface level, and the occluding contour level, and combine the results. The robust decision making allows us to combine data from multiple sensors. Sensor measurements have bounds based on the physical limitations of the sensors. We use this information without making a priori assumptions of distributions within the intervals or a priori assumptions of the probability of a given result

Detection algorithms for spatial data

This dissertation addresses the problem of anomaly detection in spatial data. The problem of landmine detection in airborne spatial data is chosen as the specific detection scenario. The first part of the dissertation deals with the development of a fast algorithm for kernel-based non-linear anomaly detection in the airborne spatial data. The original Kernel RX algorithm, proposed by Kwon et al. [2005a], suffers from the problem of high computational complexity, and has seen limited application. With the aim to reduce the computational complexity, a reformulated version of the Kernel RX, termed the Spatially Weighted Kernel RX (SW-KRX), is presented. It is shown that under this reformulation, the detector statistics can be obtained directly as a function of the centered kernel Gram matrix. Subsequently, a methodology for the fast computation of the centered kernel Gram matrix is proposed. The key idea behind the proposed methodology is to decompose the set of image pixels into clusters, and expediting the computations by approximating the effect of each cluster as a whole. The SW-KRX algorithm is implemented for a special case, and comparative results are compiled for the SW-KRX vis-à-vis the RX anomaly detector. In the second part of the dissertation, a detection methodology for buried mine detection is presented. The methodology is based on extraction of color texture information using cross-co-occurrence features. A feature selection methodology based on Bhattacharya coefficients and principal feature analysis is proposed and detection results with different feature-based detectors are presented, to demonstrate the effectiveness of the proposed methodology in the extraction of useful discriminatory information --Abstract, page iii

Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes

Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R2 value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf. The methods presented here pave the way to upscaling throughout the Landsat 8 and Sentinel-2 observational record across Antarctica to produce a first-ever continental dataset of supraglacial lake volumes. Such a dataset will improve our understanding of the influence of surface hydrology on ice shelf stability, and thus, future projections of Antarctica’s contribution to sea level rise

Towards multimodal nonlinear microscopy in clinics

Multimodal nonlinear microscopy combining two photon excited fluorescence (TPEF), second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) represents a promising and powerful tool for biomedical diagnostics. The method enables label-free visualization of morphology and chemical composition of complex tissues as well as disease related changes and is as such as detailed as staining histologic methods. In this work a compact microscope utilizing novel fiber laser sources and a new approach for data analysis based on colocalization have been developed and tested for detecting various disease patterns, e.g., atherosclerosis and brain tumors.Mit Hilfe der nichtlinearen Multikontrast-Mikroskopie basierend auf den Prozessen Zweiphotonenfluoreszenz (TPEF), Frequenzverdopplung (SHG) und kohärente anti-Stokes Raman-Streuung (CARS), können Morphologie, chemische Zusammensetzung sowie krankheitsbedingte Veränderungen komplexer Gewebe label-frei analog zu histologischen Färbungen dargestellt werden. Potentiell eignet sich die Methode daher für die in vivo Bildgebung und könnte die medizinische Diagnostik entscheidend verbessern. Im Rahmen dieser Arbeit wurde ein kompaktes TPEF/SHG/CARS-Forschungsmikroskop unter Verwendung neuer Faserlaserquellen speziell für die Verwendung in der Klinik entwickelt. Dabei wurde erforscht, wie sich der Bildkontrast durch nahinfrarote Laser sowie eine hohe spektrale Auflösung verbessern lässt. Zusätzlich wurde an Methoden der Datenanalyse multispektraler CARS-Daten gearbeitet, um mittels der Kolokalisationsanalyse die Verteilung verschiedener molekularer Marker in komplexen Geweben zu visualisieren. Das Potential für klinische Anwendungen wurde an verschiedenen Krankheitsbildern wie Arteriosklerose und Tumoren des Hirns demonstriert

JERS-1 SAR and LANDSAT-5 TM image data fusion: An application approach for lithological mapping

Satellite image data fusion is an image processing set of procedures utilise either for image optimisation for visual photointerpretation, or for automated thematic classification with low error rate and high accuracy. Lithological mapping using remote sensing image data relies on the spectral and textural information of the rock units of the area to be mapped. These pieces of information can be derived from Landsat optical TM and JERS-1 SAR images respectively. Prior to extracting such information (spectral and textural) and fusing them together, geometric image co-registration between TM and the SAR, atmospheric correction of the TM, and SAR despeckling are required. In this thesis, an appropriate atmospheric model is developed and implemented utilising the dark pixel subtraction method for atmospheric correction. For SAR despeckling, an efficient new method is also developed to test whether the SAR filter used remove the textural information or not. For image optimisation for visual photointerpretation, a new method of spectral coding of the six bands of the optical TM data is developed. The new spectral coding method is used to produce efficient colour composite with high separability between the spectral classes similar to that if the whole six optical TM bands are used together. This spectral coded colour composite is used as a spectral component, which is then fused with the textural component represented by the despeckled JERS-1 SAR using the fusion tools, including the colour transform and the PCT. The Grey Level Cooccurrence Matrix (GLCM) technique is used to build the textural data set using the speckle filtered JERS-1 SAR data making seven textural GLCM measures. For automated thematic mapping and by the use of both the six TM spectral data and the seven textural GLCM measures, a new method of classification has been developed using the Maximum Likelihood Classifier (MLC). The method is named the sequential maximum likelihood classification and works efficiently by comparison the classified textural pixels, the classified spectral pixels, and the classified textural-spectral pixels, and gives the means of utilising the textural and spectral information for automated lithological mapping

Swirl-stabilized lean-premixed flame combustion dynamics: An experimental investigation of flame stabilization, flame dynamics and combustion instability control strategies

Though modern low-emission combustion strategies have been successful in abating the emission of pollutants in aircraft engines and power generation gas turbines, combustion instability remains one of the foremost technical challenges in the development of next generation lean premixed combustor technology. Combustion instability is the coupling between unsteady heat release and combustor acoustic modes where one amplifies the other in a feedback loop. This is a complex phenomenon which involves unsteady chemical kinetic, fluid mechanic and acoustic processes that can lead to unstable behavior and could be detrimental in ways ranging from faster part fatigue to catastrophic system failure. Understanding and controlling the onset and propagation of combustion instability is therefore critical to the development of clean and efficient combustion systems. Imaging of combustion radicals has been a cornerstone diagnostic for the field of combustion for the past two decades which allows for visualization of flame structure and behavior. However, resolving both temporal and spatial structures from image-based experimental data can be very challenging. Thus, understanding flame dynamics remains a demanding task and the difficulties often lie in the chaotic and non-linear behavior of the system of interest. To this end, this work investigates the flame dynamics of lean premixed swirl stabilized flames in two distinct configurations using a variety of high fidelity optical and laser diagnostic techniques in conjunction with advanced data / algorithm based post-processing tools. The first part of this work is focused on establishing the effectiveness of microwave plasma discharges in improving combustor flame dynamics through minimizing heat release and pressure fluctuations. The effect of continuous, volumetric, direct coupled, non-equilibrium, atmospheric microwave plasma discharge on a swirl stabilized, lean premixed methane˗air flame was investigated using quantitative OH planar laser induced fluorescence (PLIF), spectrally resolved emission and acoustic pressure measurements. Proper Orthogonal Decomposition (POD) was used to post-process OH-PLIF images to extract information on flame dynamics that are usually lost through classical statistical approaches. Results show that direct plasma coupling accelerates combustion chemistry due to the non-thermal effects of plasma that lead to significantly improved combustor dynamics. Overall, this study demonstrates that microwave direct plasma coupling can drastically enhance dynamic flame stability of swirl stabilized flames especially at very lean operating conditions. The second part of this work is focused on the development of a stable and efficient small-scale combustor architecture with comparable power density, performance and emission characteristics to that of existing large-scale burners with reduced susceptibility to extinction and externally imposed acoustic perturbations while maintaining high combustion efficiency and low emission levels under ultra-lean operating conditions. Prototype burner arrays were additively manufactured, and the combustion characteristics of the mesoscale burner array were studied using several conventional and optical diagnostic techniques. The burner array was specifically configured to enhance overall combustion stability, particularly under lean operating conditions, by promoting flame to flame interactions between the neighboring elements. Dynamic mode decomposition (DMD) analysis based on high speed OH-PLIF images was carried out to provide a quantitative measure of flame stability. Results show a marked improvement in combustion stability for a mesoscale burner array compared to a single swirl-stabilized flame with similar power output. Overall, this study shows promise for integration of mesoscale combustor arrays as a flexible and scalable technology in next generation propulsion and power generation systems

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi