Abstract
According 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.
Highlights
This methodological work aims at one traditional, albeit visionary goal of the remote sensing (RS) community: the development of operational satellite-based information/knowledge processing systems, capable of automating the quantitative analysis of large-scale spaceborne multi-source multi-resolution image databases ([1]; p. 451), in compliance with the methodological guidelines of the Quality Assurance Framework for Earth Observation (QA4EO) delivered by the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observation Satellites (CEOS), the space arm of the Group on Earth Observations (GEO) [2].For publication purposes this theoretical contribution is split into two papers
The first paper [3] provides an original multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of state-of-the-art two-stage non-iterative geographic (2-D) object-based image analysis (GEOBIA) systems [4,5,6,7,8,9] and three-stage iterative geographic (2-D) object-oriented image analysis (GEOOIA) systems [4], where GEOBIA is a special case of GEOOIA, i.e., GEOOIA GEOBIA
To outperform OQIs featured by the GEOBIA/GEOOIA systems, in compliance with the GEO-CEOS QA4EO guidelines and with constraints stemming from human vision, 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 image segmentation to symbolic physical model-based image preliminary classification
Summary
This methodological work aims at one traditional, albeit visionary goal of the remote sensing (RS) community: the development of operational (good-to-go, press-and-go, turnkey) satellite-based information/knowledge processing systems (which include satellite-based measurement systems as a special case), capable of automating the quantitative analysis of large-scale spaceborne multi-source multi-resolution image databases ([1]; p. 451), in compliance with the methodological guidelines of the Quality Assurance Framework for Earth Observation (QA4EO) delivered by the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observation Satellites (CEOS), the space arm of the Group on Earth Observations (GEO) [2]. The sole exceptions these authors are aware of regard the physical model-based spectral decision-tree classifier (SPECL), implemented as a by-product in the Atmospheric/Topographic Correction (ATCOR-2/3/4) commercial software toolbox [35,36] for the estimation of biophysical variables from RS optical imagery [37,38] (for more details about SPECL, refer to Section 2 below) It means that, to the best of these authors’ knowledge, this is the first time a symbolic syntactic inference system, like SIAMTM, 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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