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

Baraldi, A.

doi:10.1109/jstars.2009.2023801

Cited by 27 publications

(56 citation statements)

References 33 publications

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“…In line with this trend, new opportunities in the design and implementation of operational hybrid RS-IUSs have been proposed to the RS community in recent years [16][17][18][19][20][21][22][23][24]. Significant contributions of these related works include the following:…”

Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

“…To reach its goal, the rest of this work is focused on the comparison, encompassing the four levels of understanding of an information processing system (refer to Section 1), of state-of-the-art GEOBIA and GEOOIA systems with the novel hybrid RS-IUS design and operational implementation proposed in [16][17][18][19][20][21][22][23][24].…”

Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

“…As proof-of-concept of symbolic deductive pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time, multi-sensor, multi-resolution, application-independent (general-purpose) Satellite Image Automatic Mapper™ (SIAM™) is selected from existing literature [16][17][18][19][20][21][22][23][24]. SIAM™ is termed 'fully automatic' because it requires neither user-defined parameters nor training data samples to run.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…In addition, no atmospheric correction was made to both the images [6]. As a consequence, the consideration that the atmosphere contributes in a different way to the reflectance measured by sensors, due to the different day of acquisition and the different flight height of sensors, should be observed.…”

Section: Analysis Of the Targets' Spectral Profilesmentioning

confidence: 99%

8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications

Tarantino¹,

Adamo²,

Pasquariello³

et al. 2012

Earth Observation

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“…A proposed instantiation of mDMI set of EO OP-Q 2 Is includes: (i) degree of automation, inversely related to human-machine interaction, (ii) effectiveness, e.g., thematic mapping accuracy, (iii) efficiency in computation time and in run-time memory occupation, e.g., inversely related to the number of system’s free-parameters to be user-defined based on heuristics, (iv) robustness (vice versa, sensitivity) to changes in input data, (v) robustness to changes in input parameters to be user-defined, (vi) scalability to changes in user requirements and in sensor specifications, (vii) timeliness from data acquisition to information product generation, (viii) costs in manpower and computer power, (ix) value, e.g., semantic value of output products, economic value of output services, etc. (Baraldi, 2017, 2009; Baraldi & Boschetti, 2012a, 2012b; Baraldi, Boschetti, & Humber, 2014; Baraldi et al, 2010a, 2010b; Baraldi, Gironda, & Simonetti, 2010c; Duke, 2016). According to the Pareto formal analysis of multi-objective optimization problems, optimization of an mDMI set of OP-Q 2 Is is an inherently-ill posed problem in the Hadamard sense (Hadamard, 1902), where many Pareto optimal solutions lying on the Pareto efficient frontier can be considered equally good (Boschetti, Flasse, & Brivio, 2004).…”

Section: Introductionmentioning

confidence: 99%

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

et al. 2018

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ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2—Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.

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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

Cited by 27 publications

References 33 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

8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

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