Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results
dc.contributor.author | Baraldi, Andrea | |
dc.contributor.author | Humber, Michael | |
dc.contributor.author | Boschetti, Luigi | |
dc.date.accessioned | 2024-01-30T16:55:14Z | |
dc.date.available | 2024-01-30T16:55:14Z | |
dc.date.issued | 2013-10-18 | |
dc.description.abstract | This paper complies with the Quality Assurance Framework for Earth Observation (QA4EO) international guidelines to provide a metrological/statistically-based quality assessment of the Spectral Classification of surface reflectance signatures (SPECL) secondary product, implemented within the popular Atmospheric/Topographic Correction (ATCOR™) commercial software suite, and of the Satellite Image Automatic Mapper™ (SIAM™) software product, proposed to the remote sensing (RS) community in recent years. The ATCOR™-SPECL and SIAM™ physical model-based expert systems are considered of potential interest to a wide RS audience: in operating mode, they require neither user-defined parameters nor training data samples to map, in near real-time, a spaceborne/airborne multi-spectral (MS) image into a discrete and finite set of (pre-attentional first-stage) spectral-based semi-concepts (e.g., “vegetation”), whose informative content is always equal or inferior to that of target (attentional second-stage) land cover (LC) concepts (e.g., “deciduous forest”). For the sake of simplicity, this paper is split into two: Part 1—Theory and Part 2—Experimental results. The Part 1 provides the present Part 2 with an interdisciplinary terminology and a theoretical background. To comply with the principle of statistics and the QA4EO guidelines discussed in the Part 1, the present Part 2 applies an original adaptation of a novel probability sampling protocol for thematic map quality assessment to the ATCOR™-SPECL and SIAM™ pre-classification maps, generated from three spaceborne/airborne MS test images. Collected metrological/statistically-based quality indicators (QIs) comprise: (i) an original Categorical Variable Pair Similarity Index (CVPSI), capable of estimating the degree of match between a test pre-classification map’s legend and a reference LC map’s legend that do not coincide and must be harmonized (reconciled); (ii) pixel-based Thematic (symbolic, semantic) QIs (TQIs) and (iii) polygon-based sub-symbolic (non-semantic) Spatial QIs (SQIs), where all TQIs and SQIs are provided with a degree of uncertainty in measurement. Main experimental conclusions of the present Part 2 are the following. (I) Across the three test images, the CVPSI values of the SIAM™ pre-classification maps at the intermediate and fine semantic granularities are superior to those of the ATCOR™-SPECL single-granule maps. (II) TQIs of both the ATCOR™-SPECL and the SIAM™ tend to exceed community-agreed reference standards of accuracy. (III) Across the three test images and the SIAM™’s three semantic granularities, TQIs of the SIAM™ tend to be significantly higher (in statistical terms) than the ATCOR™-SPECL’s. Stemming from the proposed experimental evidence in support to theoretical considerations, the final conclusion of this paper is that, in compliance with the QA4EO objectives, the SIAM™ software product can be considered eligible for injecting prior spectral knowledge into the pre-attentive vision first stage of a novel generation of hybrid (combined deductive and inductive) RS image understanding systems, capable of transforming large-scale multi-source multi-resolution EO image databases into operational, comprehensive and timely knowledge/information products. | |
dc.description.uri | https://doi.org/10.3390/rs5105209 | |
dc.identifier | https://doi.org/10.13016/dspace/tpgz-kotf | |
dc.identifier.citation | Baraldi, A.; Humber, M.; Boschetti, L. Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results. Remote Sens. 2013, 5, 5209-5264. | |
dc.identifier.uri | http://hdl.handle.net/1903/31605 | |
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 | attentive vision | |
dc.subject | confusion matrix | |
dc.subject | degree of uncertainty in measurement | |
dc.subject | harmonization (reconciliation) of ontologies | |
dc.subject | land cover classification | |
dc.subject | multi-spectral image | |
dc.subject | overlapping area matrix | |
dc.subject | preattentive vision | |
dc.subject | preliminary classification | |
dc.subject | probability sampling | |
dc.subject | quality indicators of operativeness | |
dc.subject | categorical and spatial accuracy of thematic maps | |
dc.title | Quality Assessment of Pre-Classification Maps Generated from Spaceborne/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper™ and Atmospheric/Topographic Correction™-Spectral Classification Software Products: Part 2 — Experimental Results | |
dc.type | Article | |
local.equitableAccessSubmission | No |