{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:51:25Z","timestamp":1780087885354,"version":"3.54.0"},"reference-count":95,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2012,9,14]],"date-time":"2012-09-14T00:00:00Z","timestamp":1347580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2122 (SIAM\u2122) is selected from existing literature. To the best of these authors\u2019 knowledge, this is the first time a symbolic syntactic inference system, like SIAM\u2122, 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.<\/jats:p>","DOI":"10.3390\/rs4092694","type":"journal-article","created":{"date-parts":[[2012,9,15]],"date-time":"2012-09-15T05:08:08Z","timestamp":1347685688000},"page":"2694-2735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA\/GEOOIA). Part 1: Introduction"],"prefix":"10.3390","volume":"4","author":[{"given":"Andrea","family":"Baraldi","sequence":"first","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd, Suite 209, College Park, MD 20740, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"Boschetti","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd, Suite 209, College Park, MD 20740, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2012,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gutman, G., Janetos, A.C., Justice, C.O., Moran, E.F., Mustard, J.F., Rindfuss, R.R., Skole, D., Turner, B.L., and Cochrane, M.A. (2004). 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