Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

SEPARAÇÃO DE TEMPERATURA E EMISSIVIDADE A PARTIR DE IMAGENS DO INFRAVERMELHO TERMAL: ANÁLISE DE SUAS APLICAÇÕES/RESTRIÇÕES

O infravermelho termal (TIR - ThermalInfraRed) é uma porção do espectro eletromagnético com várias aplicações no Sensoriamento Remoto, tais como: geologia, climatologia, análises de processos biológicos, análises geofísicos, avaliação de desastres e detecção de mudanças, entre outras. No TIR a emissão de radiação dos alvos é dominante, comparado com a reflexão, e esta radiação é uma função de duas variáveis, a emissividade e a temperatura do alvo. Para estudos no TIR é necessário estimar com precisão a temperatura e/ou a emissividade a partir da radiação medida, e isto é um problema devido a relação não linear existente entre estas variáveis e a radiação medida. Por isso, nos últimos 40 anos vários pesquisadores têm desenvolvido métodos visando minimizar este problema, porém, todos estes métodos possuem restrições em suas aplicações. Assim, este trabalho tem como objetivo revisar os principais métodos propostos na literaturafacilitando a sua compreensão, reprodução, além de criar umasínteseque permitaráo leitor a escolha do método mais adequado a determinadas situações

New Approaches in Airborne Thermal Image Processing for Landscape Assessment

Letecká termální hyperspektrální data přinášejí řadu informací o teplotě a emisivitě zemského povrchu. Při odhadování těchto parametrů z dálkového snímání tepelného záření je třeba řešit nedourčený systém rovnic. Bylo navrhnuto několik přístupů jak tento problém vyřešit, přičemž nejrozšířenější je algoritmus označovaný jako Temperature and Emissivity Separation (TES). Tato práce má dva hlavní cíle: 1) zlepšení algoritmu TES a 2) jeho implementaci do procesingového řetězce pro zpracování obrazových dat získaných senzorem TASI. Zlepšení algoritmu TES je možné dosáhnout nahrazením používaného modulu normalizování emisivity (tzv. Normalized Emissivity Module) částí, která je založena na vyhlazení spektrálních charakteristik nasnímané radiance. Nový modul je pak označen jako Optimized Smoothing for Temperature Emissivity Separation (OSTES). Algoritmus OSTES je připojen k procesingovému řetězci pro zpracování obrazových dat ze senzoru TASI. Testování na simulovaných datech ukázalo, že použití algoritmu OSTES vede k přesnějším odhadům teploty a emisivity. OSTES byl dále testován na datech získaných ze senzorů ASTER a TASI. V těchto případech však není možné pozorovat výrazné zlepšení z důvodu nedokonalých atmosférických korekcí. Nicméně hodnoty emisivity získané algoritmem OSTES vykazují více homogenní vlastnosti než hodnoty ze standardního produktu senzoru ASTER.Airborne thermal hyperspectral data delivers valuable information about the temperature and emissivity of the Earth's surface. However, attempting to derive temperature and emissivity from remotely sensed thermal radiation results in an underdetermined system of equations. Several approaches have been suggested to overcome this problem, but the most widespread one is called the Temperature and Emissivity Separation (TES) algorithm. This work focuses on two major topics: 1) improving the TES algorithm and 2) implementing it in a processing chain of image data acquired from the TASI sensor. The improvement of the TES algorithm is achieved by replacing the Normalized Emissivity Module with a new module, which is based on smoothing of spectral radiance signatures. The improved TES algorithm is called Optimized Smoothing for Temperature Emissivity Separation (OSTES). The OSTES algorithm is appended to a pre-processing chain of image data acquired from the TASI sensor. The testing of OSTES with simulated data shows that OSTES produces more accurate and precise temperature and emissivity retrievals. OSTES was further applied on ASTER standard products and on TASI image data. In both cases is not possible to observe significant improvement of the OSTES algorithm due to imperfect atmospheric corrections. However, the OSTES emissivitites are smoother than emissivities delivered as ASTER standard product over homogeneous surfaces.

Quantifying the spatio-temporal temperature dynamics of Greater London using thermal Earth observation

PhD ThesisUrban areas are highly sensitive to extreme events such as heatwaves. In order to understand how cities will respond to thermal stress it is critical to quantify not only their temporal temperature dynamics but also their spatial temperature variability. However, many cities lack weather station networks with a sufficient spatial distribution to characterise spatio-temporal intraurban temperature dynamics. One means by which spatially complete measurements of urban temperature may be derived is to employ satellite thermal Earth observed data. While some success has been achieved in understanding the temperature characteristics of cities using such data, relatively little work has been undertaken on establishing the use of long time-series Earth observed data as a supplement or alternative to screen-level air temperatures frequently utilised in urban climatological studies. In this thesis a software framework, centred around the use of a spatial database, is developed which can be used to gain an improved understanding of how satellite thermal Earth observed data can be used in the long timeseries analysis of urban temperature dynamics. The utility of the system is demonstrated by processing a 23 year time series (1985-2008) of 1,141 Advanced Very High Resolution Radiometer (AVHRR) images and hourly United Kingdom (UK) Met Office weather station measurements for the Greater London area. London was selected as the region of interest as it is the UK’s only megacity, and has been shown to exhibit both a significant urban heat island and a severe increase in population mortality during previous heatwave events. The software framework was employed to conduct two inter-related sets of analysis. First, the relationship over time between AVHRR estimated surface temperature (EST) and screen-level air temperature records is investigated and quantified. The resulting relationships are then used to produce an empirical model that can predict spatially complete summer-season air temperi atures for London. Cross-validation testing of the model at selected London weather stations showed model root mean square error (RMSE) ranging from 2.70 to 2.94°C and absolute errors in air temperature estimation of 0.45 to 1.67°C. A key finding of the thesis is that the minimal variation in prediction error between the different stations indicate a level of spatial robustness in the model across the urban surface, that is within the limits of the AVHRR EST precision. In addition, the model was used to estimate spatially averaged air temperatures over the Greater London area for selected summers, and showed a maximum error in air temperature prediction of 1.44°C. Furthermore, the prediction error for the heatwave summer of 2003 was 0.51°C, suggesting that such a model can successfully be used to estimate air temperatures for extreme heatwave summers. Such predictions are directly relevant to future assessments of urban population exposure to heatwaves, and it is envisaged that they could be used in conjunction with a population vulnerability index to create a spatially complete heatwave risk map for London. This work is then extended to investigate the utility of satellite estimated surface temperature measurements to characterise temporally and spatially intra-urban heatwave dynamics using the commonly employed urban heat island intensity metric (UHII). Analysis of the AVHRR EST found that the data are highly sensitive to local meteorological conditions, and that temporal aggregation at the monthly scale is required to provide robust data-sets for inter-year analysis of summer temperatures and generation of the UHII metric. Statistical testing of EST and air-temperature derived UHII for the heatwave summer of 2003 against other non-heatwave summers showed no significant increase in intensity at the 95% confidence level. This raises questions as to the applicability of the UHII metric to capture increases in urban temperatures during a heatwave event.Engineering and Physical Sciences Research Council and the School of Civil Engineering and Geoscience

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Retrieval of Urban Morphology by Means of Remote Sensing

Tämän työn tarkoituksena on luoda kaupunkialueesta morfologinen tietokanta. Kaupunkialueen morfologia sisältää tietoa kaupunkialueen ominaisuuksista. Tällaisia ominaisuuksia ovat esimerkiksi ihmislähtöinen lämpö, rakennusten sijainti, muoto ja korkeus sekä liikennevirrat. Tällaista kaupunkialueen morfologista tietokantaa voidaan käyttää esimerkiksi kaupunkisuunnittelussa tai kaupunkialueen ilmakehämallinnuksessa. Kohdealueena on Ranskan pääkaupunki Pariisi. Tietokanta on jaettu kahteen tarkkuusalueeseen, joista korkeamman tarkkuuden alue, kooltaan 6x3 km2, käsittää vain pienen osan eteläistä Pariisia, kun taas karkean tarkkuuden alueeseen, joka on kooltaan 13x10 km2, kuuluu koko Pariisin kaupunki. Tietokannan lähteenä on käytetty kaukokartoitussatelliiteista saatua tietoa, esimerkiksi optisia ja synteettisen apertuurin tutkan (SAR) kuvia, mutta myös digitaalisia karttoja. Optisten kuvien ja digitaalisten karttojen lähteinä on käytetty Google Maps- ja Microsoft Virtual Earth-palveluja, kun taas tutkakuvia on hankittu Euroopan avaruusjärjestöltä (ESA). Lisäksi on käytetty Yhdysvaltain ilmailu- ja avaruushallinnon (NASA) julkisia tietokantoja. Tietokanta rakentuu useista kerroksista. Nämä kerrokset sisältävät tietoa kaupungin rakenteesta, esimerkiksi katujen ja teiden sijainnit, puistoalueet, rakennusten sijainnit ja niiden korkeudet, puuston sijainnin sekä maanpinnan digitaalisen korkeuskartan. Nämä tiedot on irrotettu lähteistään käyttäen hyväksi niin ohjattua luokittelua kuin interferometrisen koherenssin manipulointia. Tässä työssä luotua tietokantaa voidaan käyttä hyväksi esimerkiksi rajakerroksen mallinnukseen kaupunkialueella tai hiukkasten leviämismallien simuloinneissa.The purpose of this thesis is to create an urban morphological database. An urban morphological database contains information about features of the urban area. These features consist of e.g. anthropogenic heating, location, shape and height of built structures and traffic flow. Urban morphological database can be used e.g. in city planning or in atmospheric boundary layer modeling of urban areas. The target of this database is the capital of France, Paris. The database has two resolution levels and areas. The surface area for the high resolution area is 6x3 km2 covering only a small portion of Paris, and 13x10 km2 respectively, for the coarser resolution covering the whole Paris. The database is created using optical images, digital maps and SAR interferometry i.e. it is solely based on remotely sensed data. The sources of optical images and digital maps are public Internet map services e.g. Google Maps and Microsoft Virtual Earth. Another public source for the morphological database is the National Aeronautics and Space Administration (NASA) Shuttle Radar Topography Mission (SRTM) database. A set of Synthetic Aperture Radar (SAR) images is obtained from the European Space Agency's (ESA) Envisat satellite's ASAR instrument database archives. The database consists of several layers. These layers hold information about streets and roads, parks and cemeteries, water bodies, buildings, trees, digital terrain elevation model as well as building height. The layer extraction and creation methods include supervised classification and interferometric coherence manipulation. The created database can be used e.g. in atmospheric boundary layer modeling in urban areas or in dispersion model simulations

Airborne Remote Sensing of Grimsvotn Subglacial Volcano, Vatnajokull, Iceland.

Grimsvotn, a subglacial volcano in Iceland, has a partially exposed geothermal system, that has, until recently, been used to make estimates of heat flux using calorimetry. Increased melting in Grimsvotn in the aftermath of the 1998 eruption has changed the ice conditions considerably, resulting in major leakage of the ice dam that used to seal Grimsvotn caldera lake. This makes calorimetric estimates of melting more difficult. An aerial survey of Grimsvotn was carried out in June 2001. Thermal images of the Grimsvotn subglacial caldera show distinct areas of geothermal activity. Ground survey studies of the same area carried out by the Science Institute, University of Iceland, show that protruding ground above the ice, along with areas of open water, have high geothermal heat flux all year round. In these areas, heat is lost by radiation and geothermal steam emission. This component of heat flux cannot be detected by calorimetric estimates based on ice melting. Therefore an alternative method of calculating heat flux is adopted in this research based on a combination of remote sensing and meteorological information. Aerial photographs collected for Grimsvotn have been used to map the main features along the caldera walls, such as crevasses and slumps that cannot be accurately mapped from the ground because of inaccessibility. A high resolution DEM of the case study sites has been generated from the aerial photographic coverage using a stereoscope and parallax bar. The combined data sets have been analysed both visually and quantitatively using a combination of ERDAS Imagine and ARCGIS environments. Together, these data establish that remote sensing can be used to map and monitor an inaccessible volcano such as Grimsvotn, as well as aid in the understanding of the processes at work within one of the most powerful geothermal systems in the world