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.author | Baraldi, Andrea | |
dc.contributor.author | Boschetti, Luigi | |
dc.date.accessioned | 2024-01-30T18:20:05Z | |
dc.date.available | 2024-01-30T18:20:05Z | |
dc.date.issued | 2012-09-20 | |
dc.description.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. | |
dc.description.uri | https://doi.org/10.3390/rs4092768 | |
dc.identifier | https://doi.org/10.13016/dspace/pvng-ri16 | |
dc.identifier.citation | Baraldi, 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.uri | http://hdl.handle.net/1903/31622 | |
dc.language.iso | en_US | |
dc.publisher | MDPI | |
dc.relation.isAvailableAt | College of Behavioral & Social Sciences | en_us |
dc.relation.isAvailableAt | Geography | en_us |
dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
dc.subject | categorical variable, computer vision | |
dc.subject | continuous variable | |
dc.subject | decision-tree classifier | |
dc.subject | deductive learning from rules | |
dc.subject | Geographic Object-Based Image Analysis (GEOBIA) | |
dc.subject | Geographic Object-Oriented Image Analysis (GEOOIA) | |
dc.subject | image classification | |
dc.subject | inductive learning from either labeled or unlabeled data | |
dc.subject | inference | |
dc.subject | machine learning | |
dc.subject | physical model | |
dc.subject | prior knowledge | |
dc.subject | radiometric calibration | |
dc.subject | remote sensing | |
dc.subject | Satellite Image Automatic Mapper (SIAM) | |
dc.subject | syntactic inference system | |
dc.subject | statistical model | |
dc.subject | Strengths Weakness Opportunities and Threats (SWOT) analysis of a project | |
dc.title | 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.type | Article | |
local.equitableAccessSubmission | No |