Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation

dc.contributor.authorBaraldi, Andrea
dc.contributor.authorBoschetti, Luigi
dc.date.accessioned2024-01-30T18:20:05Z
dc.date.available2024-01-30T18:20:05Z
dc.date.issued2012-09-20
dc.description.abstractAccording to 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 Quality Indexes of Operativeness (OQIs) 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. Based on an original multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the GEOBIA/GEOOIA approaches, the first part of this work 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 capable of accomplishing image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the present 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 (d) implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time, multi-sensor, multi-resolution, application-independent 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.
dc.description.urihttps://doi.org/10.3390/rs4092768
dc.identifierhttps://doi.org/10.13016/dspace/pvng-ri16
dc.identifier.citationBaraldi, A.; Boschetti, L. Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation. Remote Sens. 2012, 4, 2768-2817.
dc.identifier.urihttp://hdl.handle.net/1903/31622
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtCollege of Behavioral & Social Sciencesen_us
dc.relation.isAvailableAtGeographyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectcategorical variable, computer vision
dc.subjectcontinuous variable
dc.subjectdecision-tree classifier
dc.subjectdeductive learning from rules
dc.subjectGeographic Object-Based Image Analysis (GEOBIA)
dc.subjectGeographic Object-Oriented Image Analysis (GEOOIA)
dc.subjectimage classification
dc.subjectinductive learning from either labeled or unlabeled data
dc.subjectinference
dc.subjectmachine learning
dc.subjectphysical model
dc.subjectprior knowledge
dc.subjectradiometric calibration
dc.subjectremote sensing
dc.subjectSatellite Image Automatic Mapper (SIAM)
dc.subjectsyntactic inference system
dc.subjectstatistical model
dc.subjectStrengths Weakness Opportunities and Threats (SWOT) analysis of a project
dc.titleOperational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 2: Novel system Architecture, Information/Knowledge Representation, Algorithm Design and Implementation
dc.typeArticle
local.equitableAccessSubmissionNo

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