IMAGE2006: A component of the GMES precursor fast track service on land monitoring

Lima, Manuel; Conrad, Bielski; Nowak, Jakub Stanislaw

doi:10.1109/igarss.2007.4423392

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…In line with the QA4EO recommendations [2] the RS community regards as an indisputable fact that "the prerequisite for physically based, quantitative analysis of airborne and satellite sensor measurements in the optical domain is their calibration to spectral radiance" ( [45]; p. 29). Irrespective of this common knowledge, radiometric calibration is often neglected in the RS literature and surprisingly ignored by scientists, practitioners and institutions in RS common practice, including large-scale spaceborne image mosaicking and mapping, e.g., see [46,47]. For example, in conflict with the QA4EO guidelines, popular RS-IUS commercial software products, such as those listed in Table 1, do not consider radiometric calibration of RS imagery as a pre-requisite, with the sole exception of the physical model-based Atmospheric/Topographic Correction (ATCOR-2/3/4) commercial software [48,49].…”

Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

2012

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According to existing literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA) systems and three-stage iterative geographic object-oriented image analysis (GEOOIA) systems, where GEOOIA ⊃ GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, this methodological work is split into two parts. The first part of this work provides a multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the GEOBIA/GEOOIA approaches that augments similar analyses proposed in recent years. In line with constraints stemming from human vision, this SWOT analysis promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS) image understanding system (RS-IUS), from sub-symbolic statistical model-based (inductive) image segmentation to symbolic physical model-based (deductive) image preliminary classification. Hence, a symbolic deductive pre-attentive vision first stage accomplishes image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the second part of this work a novel hybrid (combined deductive and inductive) RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a) computational theory (system design); (b) information/knowledge representation; (c) algorithm design; and OPEN ACCESSRemote Sens. 2012, 4 2695 (d) implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time Satellite Image Automatic Mapper™ (SIAM™) is selected from existing literature. To the best of these authors' knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage, to accomplish multi-scale image segmentation and multi-granularity image pre-classification simultaneously, automatically and in near real-time.

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Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

2012

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“…The need for production of five high-resolution (HR) land cover layers emerged on behalf of the European Environment Agency (EEA): imperviousness, forest, grassland, wetland and water. The European HR Soil Sealing layer has been produced based on semi-automatic processing of IMAGE2006 data based on SPOT 4/5 and IRS-P6 fine spatial resolution optical satellite imagery (de Lima et al, 2007). The Soil Sealing layer represents a new type of information layer in the European environmental assessment, being the first example of the planned series of high-resolution Land Monitoring layers with European coverage.…”

Section: Soil Sealing Databasementioning

confidence: 99%

Dasimetrično modeliranje dinamike prebivalstva na urbanih območjih

Bajat¹,

Krunić²,

Samardžić-Petrović³

et al. 2013

Geod. vestn.

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Impact of Radiometric Calibration and Specifications of Spaceborne Optical Imaging Sensors on the Development of Operational Automatic Remote Sensing Image Understanding Systems

Baraldi

2009

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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IMAGE2006: A component of the GMES precursor fast track service on land monitoring

Cited by 4 publications

References 3 publications

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Dasimetrično modeliranje dinamike prebivalstva na urbanih območjih

Impact of Radiometric Calibration and Specifications of Spaceborne Optical Imaging Sensors on the Development of Operational Automatic Remote Sensing Image Understanding Systems

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