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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
Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd, Suite 209, College Park, MD 20740, USA
* Author to whom correspondence should be addressed.
Received: 20 July 2012; in revised form: 20 August 2012 / Accepted: 28 August 2012 / Published: 14 September 2012
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
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Cite This Article
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.
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.
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.