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

dc.contributor.authorBaraldi, Andrea
dc.contributor.authorBoschetti, Luigi
dc.date.accessioned2024-01-30T18:20:23Z
dc.date.available2024-01-30T18:20:23Z
dc.date.issued2012-09-14
dc.description.abstractAccording 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 present first paper 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 (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.
dc.description.urihttps://doi.org/10.3390/rs4092694
dc.identifierhttps://doi.org/10.13016/dspace/pqo3-kmfr
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 1: Introduction. Remote Sens. 2012, 4, 2694-2735.
dc.identifier.urihttp://hdl.handle.net/1903/31623
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.subjecthuman vision
dc.subjectimage classification
dc.subjectinductive learning from either labeled (supervised) or unlabeled (unsupervised) data
dc.subjectinference
dc.subjectmachine learning
dc.subjectphysical model
dc.subjectpre-attentive and attentive vision
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 1: Introduction
dc.typeArticle
local.equitableAccessSubmissionNo

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