{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:33:25Z","timestamp":1760243605068,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2013,10,18]],"date-time":"2013-10-18T00:00:00Z","timestamp":1382054400000},"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>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\u2122) commercial software suite, and of the Satellite Image Automatic Mapper\u2122 (SIAM\u2122) software product, proposed to the remote sensing (RS) community in recent years. The ATCOR\u2122-SPECL and SIAM\u2122 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., \u201cvegetation\u201d), whose informative content is always equal or inferior to that of target (attentional second-stage) land cover (LC) concepts (e.g., \u201cdeciduous forest\u201d). For the sake of simplicity, this paper is split into two: Part 1\u2014Theory and Part 2\u2014Experimental 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\u2122-SPECL and SIAM\u2122 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\u2019s legend and a reference LC map\u2019s 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\u2122 pre-classification maps at the intermediate and fine semantic granularities are superior to those of the ATCOR\u2122-SPECL single-granule maps. (II) TQIs of both the ATCOR\u2122-SPECL and the SIAM\u2122 tend to exceed community-agreed reference standards of accuracy. (III) Across the three test images and the SIAM\u2122\u2019s three semantic granularities, TQIs of the SIAM\u2122 tend to be significantly higher (in statistical terms) than the ATCOR\u2122-SPECL\u2019s. 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\u2122 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.<\/jats:p>","DOI":"10.3390\/rs5105209","type":"journal-article","created":{"date-parts":[[2013,10,18]],"date-time":"2013-10-18T12:05:51Z","timestamp":1382097951000},"page":"5209-5264","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Quality Assessment of Pre-Classification Maps Generated from Spaceborne\/Airborne Multi-Spectral Images by the Satellite Image Automatic Mapper\u2122 and Atmospheric\/Topographic Correction\u2122-Spectral Classification Software Products: Part 2 \u2014 Experimental Results"],"prefix":"10.3390","volume":"5","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":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Humber","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, 4321 Hartwick Rd, Suite 209, College Park, MD 20740, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Boschetti","sequence":"additional","affiliation":[{"name":"College of Natural Resources, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,10,18]]},"reference":[{"key":"ref_1","unstructured":"Global Earth Observation (GEO) The Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan, 16 February 2005. Available online: http:\/\/www.earthobservations.org\/docs\/10-Year%20Implementation%20Plan.pdf."},{"key":"ref_2","unstructured":"Global Earth Observation (GEO)\/Committee on Earth Observation Satellites (CEOSS) Available online: http:\/\/qa4eo.org\/docs\/QA4EO_Principles_v4.0.pdf."},{"key":"ref_3","unstructured":"Committee on Earth Observation Satellites (CEOS) CEOS Working Group on Calibration and Validation\u2014Land Product Validation Subgroup. Available online: http:\/\/lpvs.gsfc.nasa.gov\/."},{"key":"ref_4","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). Land Change Science, Kluwer.","DOI":"10.1007\/978-1-4020-2562-4"},{"key":"ref_5","unstructured":"Marr, D (1982). Vision, W.H. Freeman and Company."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.3390\/rs1041139","article-title":"Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach","volume":"1","author":"Dorigo","year":"2009","journal-title":"Remote Sens"},{"key":"ref_7","unstructured":"Richter, R., and Schl\u00e4pfer, D Available online: http:\/\/www.rese.ch\/pdf\/atcor3_manual.pdf."},{"key":"ref_8","unstructured":"Richter, R., and Schl\u00e4pfer, D Available online: http:\/\/www.rese.ch\/pdf\/atcor4_manual.pdf."},{"key":"ref_9","unstructured":"Schl\u00e4pfer, D., Richter, R., and Hueni, A (2009, January 16\u201319). Recent Developments in Operational Atmospheric and Radiometric Correction of Hyperspectral Imagery. Tel-Aviv, Israel."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/JSTARS.2009.2023801","article-title":"Impact of radiometric calibration and specifications of spaceborne optical imaging sensors on the development of operational automatic remote sensing image understanding systems","volume":"2","author":"Baraldi","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/TGRS.2006.874140","article-title":"Automatic spectral rule-based preliminary mapping of calibrated Landsat TM and ETM+ images","volume":"44","author":"Baraldi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TGRS.2009.2032457","article-title":"Automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4\/-5\/IRS, AVHRR\/MSG, AATSR, IKONOS\/QuickBird\/OrbView\/GeoEye and DMC\/SPOT-1\/-2 imagery\u2014Part I: System design and implementation","volume":"48","author":"Baraldi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1109\/TGRS.2009.2032064","article-title":"Automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4\/-5\/IRS, AVHRR\/MSG, AATSR, IKONOS\/QuickBird\/OrbView\/GeoEye and DMC\/SPOT-1\/-2 imagery\u2014Part II: Classification accuracy assessment","volume":"48","author":"Baraldi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1109\/TGRS.2010.2042132","article-title":"Corrections to Automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4\/-5\/IRS, AVHRR\/MSG, AATSR, IKONOS\/QuickBird\/OrbView\/GeoEye and DMC\/SPOT-1\/-2 Imagery","volume":"48","author":"Baraldi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TGRS.2009.2028017","article-title":"Operational two-stage stratified topographic correction of spaceborne multi-spectral imagery employing an automatic spectral rule-based decision-tree preliminary classifier","volume":"48","author":"Baraldi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3482","DOI":"10.1109\/TGRS.2010.2046741","article-title":"Operational performance of an automatic preliminary spectral rule-based decision-tree classifier of spaceborne very high resolution optical images","volume":"48","author":"Baraldi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1109\/TGRS.2010.2091137","article-title":"Fuzzification of a crisp near-real-time operational automatic spectral-rule-based decision-tree preliminary classifier of multisource multispectral remotely sensed images","volume":"49","author":"Baraldi","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2694","DOI":"10.3390\/rs4092694","article-title":"Operational automatic remote sensing image understanding systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA\/GEOOIA)\u2014Part 1: Introduction","volume":"4","author":"Baraldi","year":"2012","journal-title":"Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.3390\/rs4092768","article-title":"Operational automatic remote sensing image understanding systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA\/GEOOIA)\u2014Part 2: Novel system architecture, information\/knowledge representation, algorithm design and implementation","volume":"4","author":"Baraldi","year":"2012","journal-title":"Remote Sens"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Baraldi, A., Humber, M., and Boschetti, L (Remote Sens, 2013). Quality assessment of pre-classification maps generated from spaceborne\/airborne multi-spectral images by the Satellite Image Automatic Mapper\u2122 and Atmospheric\/Topographic Correction-Spectral Classification software products: Part 1\u2014Theory, submitted for consideration for publication, Remote Sens, submitted.","DOI":"10.3390\/rs5105209"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1109\/TGRS.2007.908876","article-title":"SAR sea-ice image analysis based on iterative region growing using semantics","volume":"45","author":"Yu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_22","unstructured":"Cherkassky, V., and Mulier, F (1998). Learning from Data: Concepts, Theory, and Methods, Wiley."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Clarendon Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0034-4257(98)00010-8","article-title":"Design and analysis for thematic map accuracy assessment: Fundamental principles","volume":"64","author":"Stehman","year":"1998","journal-title":"Remote Sens. Environ"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/00031305.1995.10476160","article-title":"The Horvitz-Thompson theorem as a unifying perspective for probability sampling: With examples from natural resource sampling","volume":"49","author":"Overton","year":"1995","journal-title":"Am. Stat"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1002\/aris.1440370109","article-title":"The concept of information","volume":"37","author":"Capurro","year":"2003","journal-title":"Annu. Rev. Inform. Sci. Technol"},{"key":"ref_27","first-page":"79","article-title":"Hermeneutics and the Phenomenon of Information","volume":"19","author":"Capurro","year":"2000","journal-title":"Metaphysics, Epistemology, and Technology: Research in Philosophy and Technology"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Laurini, R., and Thompson, D (1992). Fundamentals of Spatial Information Systems, Academic Press.","DOI":"10.1016\/B978-0-08-092420-5.50014-1"},{"key":"ref_29","unstructured":"Mather, P (1994). Computer Processing of Remotely-Sensed Images\u2014An Introduction, John Wiley & Sons."},{"key":"ref_30","unstructured":"Matsuyama, T., and Hwang, V.S. (1990). SIGMA: A Knowledge-Based Aerial Image Understanding System, Plenum Press."},{"key":"ref_31","unstructured":"Sonka, M., Hlavac, V., and Boyle, R (2008). Image Processing and Machine Vision, Thompson Learning."},{"key":"ref_32","unstructured":"Baraldi, A., Boschetti, L., and Humber, M (2014). IEEE Trans. Geosci. Remote Sens., in press."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"249","DOI":"10.2747\/1548-1603.45.3.249","article-title":"Importance of matrix construction for multiple-resolution categorical map comparison","volume":"45","author":"Kuzera","year":"2008","journal-title":"GIScience Remote Sens"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3044","DOI":"10.1016\/j.rse.2011.06.007","article-title":"Pixels, blocks of pixels, and polygons: Choosing a spatial unit for thematic accuracy assessment","volume":"115","author":"Stehman","year":"2011","journal-title":"Remote Sens. Environ"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K (1999). Assessing the Accuracy of Remotely Sensed Data, Lewis Publishers.","DOI":"10.1201\/9781420048568"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1080\/014311699212065","article-title":"Comparing thematic maps based on map value","volume":"20","author":"Stehman","year":"1999","journal-title":"Int. J. Remote Sens"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1016\/j.rse.2007.08.012","article-title":"Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: A study of 1992 and 2001 US National Land Cover Database changes","volume":"112","author":"Ahlqvist","year":"2008","journal-title":"Remote Sens. Environ"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1109\/TGRS.2006.871219","article-title":"A joint initiative for harmonization and validation of land cover datasets","volume":"44","author":"Herold","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0198-9715(03)00020-6","article-title":"Assessment of semantic similarity between land use\/land cover classification systems","volume":"28","author":"Feng","year":"2004","journal-title":"Comput. Environ. Urban Syst"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1080\/13658810210129120","article-title":"A method for the formalization and integration of geographical categorizations","volume":"16","author":"Kavouras","year":"2002","journal-title":"Int. J. Geogr. Inf. Sci"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1111\/1467-9671.00109","article-title":"Using ontologies for integrated geographic information systems","volume":"6","author":"Fonseca","year":"2002","journal-title":"Trans. GIS"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1015808104769","article-title":"Semantic granularity in ontology-driven geographic information systems","volume":"36","author":"Fonseca","year":"2002","journal-title":"AMAI Ann. Math. Artif. Intell"},{"key":"ref_44","unstructured":"Cerba, O., Charvat, K., and Jezek, J Data Harmonization towards CORINE Land Cover. Available online: www.efita.net\/apps\/accesbase\/bindocload.asp."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/13658810600965271","article-title":"Towards a general theory of geographic representation in GIS","volume":"21","author":"Goodchild","year":"2007","journal-title":"Int. J. Geogr. Inf. Sci"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(94)00098-8","article-title":"Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon","volume":"52","author":"Adams","year":"1995","journal-title":"Remote Sens. Environ"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1080\/13658810500106729","article-title":"Using uncertain conceptual spaces to translate between land cover categories","volume":"19","author":"Ahlqvist","year":"2005","journal-title":"Int. J. Geogr. Inf. Sci"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.1080\/014311697216720","article-title":"The evaluation of segmentation results and the overlapping area matrix","volume":"18","author":"Beauchemin","year":"2010","journal-title":"Int. J. Remote Sens"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1109\/TGRS.2004.843074","article-title":"Quality assessment of classification and cluster maps without ground truth knowledge","volume":"43","author":"Baraldi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_50","unstructured":"Lunetta, R.S., and Elvidge, C.D. (1999). Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, Taylor & Francis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ"},{"key":"ref_52","unstructured":"Anonymous FTP Available online: ftp:\/\/ftp.iluci.org\/Paper\/remotesensing-29006_2013."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_54","first-page":"1011","article-title":"Quantification error versus location error in comparison of categorical maps","volume":"66","author":"Pontius","year":"2000","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1109\/TGRS.2009.2029570","article-title":"A novel protocol for accuracy assessment in classification of very high resolution images","volume":"48","author":"Persello","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens"},{"key":"ref_57","unstructured":"Pontius, R.G., and Connors, J (2006, January 5\u20137). Expanding the Conceptual, Mathematical and Practical Methods for Map Comparison. Lisbon, Portugal."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/36.739098","article-title":"Accuracy and inaccuracy assessments in landcover classification","volume":"37","author":"Nishii","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer-Verlag.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_60","unstructured":"Definiens Imaging GmbH (2004). eCognition Elements User Guide 4, Definiens Imaging GmbH."},{"key":"ref_61","unstructured":"Definiens, A.G. (2011). Developer 8 Reference Book, Definiens AG."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/LGRS.2008.919622","article-title":"Improvement of image segmentation accuracy based on multiscale optimization procedure","volume":"5","author":"Esch","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_63","first-page":"12","article-title":"Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation","volume":"58","author":"Strobl","year":"2000","journal-title":"Angewandte Geographische Informationsverarbeitung XII"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer-Verlag.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_65","unstructured":"Trimble eCognition Developer. Available online: http:\/\/www.ecognition.com\/products\/ecognition-developer."},{"key":"ref_66","unstructured":"Hay, G.J., and Castilla, G (2006, January 4\u20135). Object-Based Image Analysis: Strengths, Weaknesses, Opportunities and Threats (SWOT). Salzburg, Austria."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"McGlone, J.C., and Shufelt, J.A. (1994, January 21\u201323). Projective and Object Space Geometry for Monocular Building Extraction. Seattle, WA, USA.","DOI":"10.1109\/CVPR.1994.323810"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.3390\/rs3061188","article-title":"Evaluation of automatic building detection approaches combining high resolution images and LiDAR data","volume":"3","author":"Hermosilla","year":"2011","journal-title":"Remote Sens"},{"key":"ref_69","first-page":"49","article-title":"Sur les problemes aux derivees partielles et leur signification physique","volume":"13","author":"Hadamard","year":"1902","journal-title":"Princet. Univ. Bull"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/10\/5209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:49:59Z","timestamp":1760219399000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/10\/5209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,10,18]]},"references-count":69,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2013,10]]}},"alternative-id":["rs5105209"],"URL":"https:\/\/doi.org\/10.3390\/rs5105209","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2013,10,18]]}}}