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Remote Sens. 2012, 4(9), 2694-2735; doi:10.3390/rs4092694
Article

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

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Received: 20 July 2012; in revised form: 20 August 2012 / Accepted: 28 August 2012 / Published: 14 September 2012
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Abstract: 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 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.
Keywords: categorical variable; computer vision; continuous variable; decision-tree classifier; deductive learning from rules; Geographic Object-Based Image Analysis (GEOBIA); Geographic Object-Oriented Image Analysis (GEOOIA); human vision; image classification; inductive learning from either labeled (supervised) or unlabeled (unsupervised) data; inference; machine learning; physical model; pre-attentive and attentive vision; prior knowledge; radiometric calibration; remote sensing; Satellite Image Automatic Mapper™ (SIAM™); syntactic inference system; statistical model; Strengths Weakness Opportunities and Threats (SWOT) analysis of a project categorical variable; computer vision; continuous variable; decision-tree classifier; deductive learning from rules; Geographic Object-Based Image Analysis (GEOBIA); Geographic Object-Oriented Image Analysis (GEOOIA); human vision; image classification; inductive learning from either labeled (supervised) or unlabeled (unsupervised) data; inference; machine learning; physical model; pre-attentive and attentive vision; prior knowledge; radiometric calibration; remote sensing; Satellite Image Automatic Mapper™ (SIAM™); syntactic inference system; statistical model; Strengths Weakness Opportunities and Threats (SWOT) analysis of a project
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Baraldi, 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.

AMA Style

Baraldi 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 Sensing. 2012; 4(9):2694-2735.

Chicago/Turabian Style

Baraldi, Andrea; Boschetti, Luigi. 2012. "Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction." Remote Sens. 4, no. 9: 2694-2735.


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