10 research outputs found

    Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

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    Over the past decade, number of optical Earth observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (e.g., radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lacks on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (e.g., absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with its specifications or requirements. In cases of multiple methods, recommendation has been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that needs to be assessed to evaluate remote sensing satellite data quality, and best vicarious methodologies to evaluate identified quality parameters such as coherent noise, ground sample distance, etc

    Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

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    Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

    Worldwide Optimal PICS Search

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    Pseudo Invariant Calibration Sites (PICS) have proven to be a dependable calibration source for determining degradation of visible and infrared sensor response due to their temporal stability and spatial uniformity. One limit of PICS is that only a handful have been identified, primarily in desert areas of North Africa, Saudi Arabia, and elsewhere. A large number of PICS would not only facilitate calibration of existing and future sensors, but also provide an alternative to internal on-board calibrator data, resulting in significant cost savings and simplification in sensor design. As a result, the process to efficiently identify additional PICS is highly desirable. A relatively straightforward algorithm and processing flow to identify candidate PICS throughout the world has been developed. One goal of the algorithm is to identify PICS with reflectance levels covering more of the sensor dynamic range. As currently implemented, the algorithm makes use of Google Earth Engine to simplify the required image data pre-processing, analysis, and storage, and implements a filtering technique to enhance contiguity of pixels identified as invariant. Application of the proposed algorithm identified not only existing North Africa and Middle East sites with 2% to 2.5% temporal uncertainty, but also sites on other continents with 5% to 6% uncertainty, which can be improved with application of BRDF correction. In general, the algorithm shows potential in providing a means for automated PICS identification

    Band to Band Calibration and Relative Gain Analysis of Satellite Sensors Using Deep Convective Clouds

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    Two calibration techniques were developed in this research. First, a calibration technique, in which calibration was transferred to cirrus band and coastal aerosol band from well calibrated reflective bands of Landsat 8 using SCIAMACHY Deep Convective Cloud (DCC) spectra. Second, a novel method to derive relative gains using DCCs and improve the image quality of cirrus band scenes was developed. DCCs are very cold, bright clouds located in the tropopause layer. At small sun elevation and sensor viewing angles, they act as near Lambertian solar reflectors. They have very high signal to noise ratio and can easily be detected using simple IR threshold. Thus, DCCs are an ideal calibration target. Cirrus band in Landsat 8 has band center at 1375nm. Due to high water vapor absorption at this wavelength it is difficult to calibrate the cirrus band using other standard vicarious calibration methods. Similarly, the coastal aerosol band has short wavelength (443nm). At this wavelength maximum scattering can be observed in the atmosphere, due to which it is difficult to calibrate this band. Thus DCCs are investigated to calibrate these two channels. DCC spectra measured by the SCIAMACHY hyperspectral sensor were used to transfer calibration. The gain estimates after band to band calibration using DCC for the coastal aerosol band was 0.986 ±0.0031 and that for cirrus band was 0.982±0.0398. The primarily target was to estimate gains with uncertainty of less than 5%. The results are within required precision levels and the primarily goal of the research was successfully accomplished. The non-uniformity in detector response can cause visible streaks in the image. To remove these visible streaks, modified histogram equalization method was used in the second algorithm. A large number of DCC scenes were binned and relative gains were derived. Results were validated qualitatively by visual analysis and quantitatively by the streaking metric. The streaking metric was below 0.2 for most of the detector which was the required goal. Visible streaks were removed by applying DCC derived gains and in most of the cases DCC gains outperforms the default gains

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

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    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

    Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

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    An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS

    Workshop on Strategies for Calibration and Validation of Global Change Measurements

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    The Committee on Environment and Natural Resources (CENR) Task Force on Observations and Data Management hosted a Global Change Calibration/Validation Workshop on May 10-12, 1995, in Arlington, Virginia. This Workshop was convened by Robert Schiffer of NASA Headquarters in Washington, D.C., for the CENR Secretariat with a view toward assessing and documenting lessons learned in the calibration and validation of large-scale, long-term data sets in land, ocean, and atmospheric research programs. The National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC) hosted the meeting on behalf of the Committee on Earth Observation Satellites (CEOS)/Working Group on Calibration/walidation, the Global Change Observing System (GCOS), and the U. S. CENR. A meeting of experts from the international scientific community was brought together to develop recommendations for calibration and validation of global change data sets taken from instrument series and across generations of instruments and technologies. Forty-nine scientists from nine countries participated. The U. S., Canada, United Kingdom, France, Germany, Japan, Switzerland, Russia, and Kenya were represented

    Using middle-infrared reflectance for burned area detection

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    Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Meteorologia), Universidade de Lisboa, Faculdade de Ciências, 2011A strategy is presented that allows deriving a new index for burned area discrimination over the Amazon and Cerrado regions of Brazil. The index is based on information from the near-infrared (NIR) and middle-infrared (MIR) channels of the Moderate Resolution Imaging Spectroradiometer (MODIS). A thorough review is undertaken of existing methods for retrieving MIR reflectance and an assessment is performed, using simulated and real data, about the added value obtained when using the radiative transfer equation (RTE) instead of the simplified algorithm (KR94) developed by Kaufman and Remer (1994), the most used in the context of burned area studies. It is shown that use of KR94 in tropical environments to retrieve vegetation reflectance may lead to errors that are at least of the same order of magnitude of the reflectance to be retrieved and considerably higher for large values of land surface temperature (LST) and solar zenith angle (SZA). Use of the RTE approach leads to better estimates in virtually all cases, with the exception of high values of LST and SZA, where results from KR94 are also not usable. A transformation is finally defined on the MIR/NIR reflectance space aiming to enhance the spectral information such that vegetated and burned surfaces may be effectively discriminated. The transformation is based on the difference between MIR and NIR in conjunction with the distance from a convergence point in the MIR/NIR space, representative of a totally burnt surface. The transformation allows defining a system of coordinates, one coordinate having a small scatter for pixels associated to vegetation, burned surfaces and soils containing organic matter and the other coordinate covering a wide range of values, from green and dry/stressed vegetation to burned surfaces. The new set of coordinates opens interesting perspectives to applications like drought monitoring and burned area discrimination using remote-sensed information.O coberto vegetal da superfície da Terra tem vindo a sofrer mudanças, por vezes drásticas, que conduzem a alterações tanto na rugosidade da superfície terrestre como no seu albedo, afectando directamente as trocas de calor sensível e latente e de dióxido de carbono entre a superfície terrestre e a atmosfera (Sellers et al., 1996). Neste contexto, as queimadas assumem um papel de extremo relevo (Nobre et al., 1991; O’Brien, 1996; Xue, 1996) na medida em que constituem uma das mais importantes fontes de alteração do coberto vegetal, resultando na destruição de florestas e de recursos naturais, libertando carbono da superfície continental para a atmosfera (Sellers et al., 1995) e perturbando as interacções biosfera-atmosfera (Levine et al., 1995; Scholes, 1995) através de mudanças na rugosidade do solo, na área foliar e noutros parâmetros biofísicos associados ao coberto vegetal. Ora, neste particular, a Amazónia Brasileira constitui um exemplo notável de mudanças no uso da terra e do coberto vegetal nas últimas décadas, como resultado da desflorestação induzida pelo homem bem como por causas naturais (Gedney e Valdes, 2000; Houghton, 2000; Houghton et al., 2000; Lucas et al., 2000), estimando-se que as regiões tropicais sejam responsáveis por cerca de 32% da emissão global de carbono para a atmosfera (Andreae, 1991). Neste contexto, a disponibilidade de informações pormenorizadas e actualizadas sobre as distribuições espacial e temporal de queimadas e de áreas ardidas em regiões tropicais afigura-se crucial, não só para uma melhor gestão dos recursos naturais, mas também para estudos da química da atmosfera e de mudanças climáticas (Zhan et al., 2002). A detecção remota constitui, neste âmbito, uma ferramenta indispensável na medida em que permite uma monitorização em tempo quase real, a qual se revela especialmente útil em áreas extensas e/ou de difícil acesso afectadas pelo fogo (Pereira et al., 1997). Diversos instrumentos, tais como o Land Remote Sensing Satellite/Thematic Mapper (LANDSAT/TM) e o National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) têm vindo a ser extensivamente utilizados na gestão dos fogos florestais, em particular aos níveis da detecção de focos de incêndio e da monitorização de áreas queimadas. Mais recentemente, o instrumento VEGETATION a bordo do Satellite Pour l'Observation de la Terre (SPOT) tem vindo a ser utilizado com sucesso na monitorização de fogos. Finalmente, são de referir os sensores da série Along Track Scanning Radiometer (ATSR) para os quais têm vindo a ser desenvolvidos algoritmos de identificação de focos de incêndio, e ainda o sensor Moderate Resolution Imaging Spectroradiometer (MODIS) que tem vindo a demonstrar capacidades óptimas no que respeita à observação global de fogos, plumas e áreas queimadas. Neste contexto, os métodos actuais de detecção de áreas ardidas através da detecção remota têm vindo a dar prioridade à utilização das regiões do vermelho (0.64 μm) e infravermelho-próximo (0.84 μm) do espectro eletromagnético. No entanto, tanto a região do vermelho quanto a do infravermelho-próximo apresentam a desvantagem de serem sensíveis à presença de aerossóis na atmosfera (Fraser e Kaufman, 1985; Holben et. al., 1986). Desta forma, em regiões tropicais como a Amazónia, onde existem grandes camadas de fumo devido à queima de biomassa, a utlização destas duas regiões do espectro eletromagnético torna-se insatisfatória para a detecção de áreas ardidas. Por outro lado, a região do infravermelho médio (3.7 – 3.9 μm) tem a vantagem de não ser sensível à presença da maior parte dos aerossóis, exceptuando a poeira (Kaufman e Remer, 1994) mostrando-se, ao mesmo tempo, sensível a mudanças na vegetação devido à absorção de água líquida. Com efeito, estudos acerca dos efeitos do vapor de água na atenuação do espectro eletromagnético demonstraram que a região do infravermelho médio é uma das únicas regiões com relativamente pouca atenuação (Kerber e Schut, 1986). Acresce que a região do infravermelho médio apresenta uma baixa variação da irradiância solar (Lean, 1991), tendo-se ainda que a influência das incertezas da emissividade na estimativa da temperatura da superfície é pequena quando comparada com outras regiões térmicas tais como as de 10.5 e 11.5 μm (Salysbury e D’Aria, 1994). A utilização da radiância medida através de satélites na região do infravermelho médio é, no entanto, dificultada pelo facto de esta ser afectada tanto pelo fluxo térmico quanto pelo fluxo solar, contendo, desta forma, duas componentes, uma emitida e outra reflectida, tendo-se que a componente reflectiva contém os fluxos térmico e solar reflectidos pela atmosfera e pela superfície enquanto que as emissões térmicas são oriundas da atmosfera e da superfície. Ora, a componente solar reflectida é de especial interesse para a detecção de áreas ardidas pelo que se torna necessário isolá-la do sinal total medido pelo sensor. Devido à ambiguidade deste sinal, a distinção dos efeitos da reflectância e da temperatura torna-se uma tarefa muito complexa, verificando-se que os métodos em que se não assume nenhuma simplificação, levando-se, portanto, em consideração todos os constituintes do sinal do infravermelho médio se tornam complexos e difíceis de serem aplicados na prática, na medida em que requerem dados auxiliares (e.g. perfis atmosféricos) e ferramentas computacionais (e.g. modelos de tranferência radiativa). Kaufman e Remer (1994) desenvolveram um método simples para estimar a reflectância do infravermelho médio o qual assenta em diversas hipóteses simplificadoras. Apesar do objectivo primário que levou ao desenvolvimento do método ser a identificação de áreas cobertas por vegetação densa e escura em regiões temperadas, este método tem sido lagarmente utilizado nos estudos acerca da discriminação de áreas queimadas, algumas das vezes em regiões tropicais (Roy et al., 1999; Barbosa et al., 1999; Pereira, 1999). Na literatura não existe, no entanto, nenhum estudo acerca da exactidão e precisão deste método quando aplicado com o objectivo de detectar áreas ardidas, em especial em regiões tropicais. Neste sentido, no presente trabalho procedeu-se a um estudo de viabilidade do método proposto por Kaufman e Remer (1994) em simultâneo com a análise da equação de tranferência radiativa na região do infravermelho médio, tendo sido realizados testes de sensibilidade dos algoritmos em relação aos erros nos perfis atmosféricos, ruído do sensor e erros nas estimativas da temperatura da superfície. Para tal recorreu-se ao modelo de transferência radiativa Moderate Spectral Resolution Atmospheric Transmittance and Radiance Code (MODTRAN), dando-se especial atenção ao caso do sensor MODIS. Os resultados demonstraram que a utilização do método proposto por Kaufman e Remer (1994) em regiões tropicais para a estimativa da reflectância no infravermelho médio, leva a erros que são pelo menos da mesma ordem de magnitude do parâmetro estimado e, em alguns casos, muito maior, quando ocorre a combinação de altas temperaturas da superfície terrestre com baixos ângulos zenitais solares. A utilização da equação de transferência radiativa mostrouse uma boa alternativa, desde que estejam disponíveis dados acerca da temperatura da superfíce terrestre assim como dos perfis atmosféricos. Entretanto, nas regiões onde ocorrem altos valores de temperatura da superfície terrestre e baixos ângulos zenitais solares, quaisquer dos dois métodos se mostra pouco utilizável, já que nesta região a estimativa da reflectância constitui um problema mal-posto. Em paralelo, utilizaram-se informações sobre aerossóis de queimada para efectuar simulações do MODTRAN que permitiram avaliar a reposta do canal do infravermelho-médio à este tipo de perturbação do sinal, muito comum na Amazónia Brasileira. A fim de tornar o estudo o mais realístico possível, procedeu-se à coleta de material resultante de queimadas na região Amazónica, mais especificamente em Alta Floresta, Mato Grosso, Brasil. Estes resultado foram então integrados nos estudos em questão, possibilitando a caracterização espectral das áreas ardidas. Com base nos resultados obtido definiu-se uma tranformação no espaço do infravermelho próximo e médio com o objetivo de maximizar a informação espectral de forma a que as superfícies vegetadas pudessem ser efectivamente discriminadas e as áreas ardidas identificadas. A tranformação baseia-se na diferença entre a reflectância nos infravermelhos próximo e médio, em conjunto com a distância a um ponto de convergência no espaço espectral dos infravermelhos próximo e médio, ponto esse representativo de uma área completamente ardida. A tranformação permitiu a definição de um novo sistema de coordenadas, o qual provou ser bastante útil no que diz respeito á identificação de áreas ardidas. Este novo espaço de coordenadas constitui uma inovação na área dos estudos de queimadas, já que permite ao mesmo tempo definir dois tipos de índices, o primeiro dos quais identifica superfícies que contém ou não biomassa e o segundo identifica, de entre as superfícies que contêm biomassa, a quantidade de água presente, podendo variar de vegetação verde (abundância de água) até áreas ardidas (ausência de água). Além de distiguir áreas ardidas, os índices desenvolvidos podem ainda ser aplicados em outros casos como, por exemplo, estudos de estresse hídrico e secas.DSA/INPE; Portuguese Foundation of Science and Technology (Fundação para a Ciência e Tecnologia / FCT)(SFRH/BD/21650/2005

    GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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    Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

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    By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations
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