828
Views
3
CrossRef citations to date
0
Altmetric
Research Article

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

ORCID Icon, , ORCID Icon & | (Reviewing Editor)
Article: 1467357 | Received 01 Aug 2017, Accepted 14 Apr 2018, Published online: 10 Jun 2018

References

  • Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Susstrunk, S. (2011). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions Pattern Analysis Machine Intelligent, 6(1), 1–8.
  • Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., & Gumley, L. E. (1998). Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 103(32), 141–157. doi:10.1029/1998JD200032
  • Adams, J. B., Donald, E. S., Kapos, V., Almeida Filho, R., Roberts, D. A., Smith, M. O., & Gillespie, A. R. (1995). Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote Sensing Environment, 52, 137–154. doi:10.1016/0034-4257(94)00098-8
  • Ahlqvist, O. (2005). Using uncertain conceptual spaces to translate between land cover categories. Intrenational Journal of Geographical Information Science, 19, 831−857.
  • Ahlqvist, O. (2008). In search of classification that supports the dynamics of science: The FAO Land Cover Classification System and proposed modifications. Environment and Planning B: Planning and Design, 35, 169–186. doi:10.1068/b3344
  • Arvor, D., Madiela, B. D., & Corpetti, T. (2016). Semantic pre-classification of vegetation gradient based on linearly unmixed Landsat time series. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International (pp. 4422–4425).
  • Baraldi, A. (2009). Impact of radiometric calibration and specifications of spaceborne optical imaging sensors on the development of operational automatic remote sensing image understanding systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(2), 104–134. doi:10.1109/JSTARS.2009.2023801
  • Baraldi, A. (2011). Fuzzification of a crisp near-real-time operational automatic spectral-rule-based decision-tree preliminary classifier of multisource multispectral remotely sensed images. IEEE Transactions on Geoscience and Remote Sensing, 49, 2113–2134. doi:10.1109/TGRS.2010.2091137
  • Baraldi, A. (2015). “Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images – AutoCloud+,” Invitation to tender ESA/AO/1-8373/15/I-NB – “VAE: Next Generation EO-based Information Services”, DOI: 10.13140/RG.2.2.34162.71363. arXiv: 1701.04256. Retrieved Jan., 8, 2017 from https://arxiv.org/ftp/arxiv/papers/1701/1701.04256.pdf
  • Baraldi, A., 2017. “Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations”. Ph.D. dissertation Agricultural and Food Sciences, University of Naples “Federico II”, Department of Agricultural Sciences, Italy. Ph.D. defense: 16 May 2017. Retrieved January 30, 2018, from https://www.researchgate.net/publication/317333100_Pre-processing_classification_and_semantic_querying_of_large-scale_Earth_observation_spaceborneairborneterrestrial_image_databases_Process_and_product_innovations doi:10.13140/RG.2.2.25510.52808
  • Baraldi, A., & Boschetti, L. (2012a). Operational automatic remote sensing image understanding systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA) - Part 1: Introduction. Remote Sensing, 4, 2694–2735. doi:10.3390/rs4092694
  • Baraldi, A., & Boschetti, L. (2012b). 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 Sensing, 4, 2768–2817.
  • Baraldi, A., Boschetti, L., & Humber, M. (2014). Probability sampling protocol for thematic and spatial quality assessment of classification maps generated from spaceborne/airborne very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 701–760. doi:10.1109/TGRS.2013.2243739
  • Baraldi, A., Bruzzone, L., & Blonda, P. (2005). Quality assessment of classification and cluster maps without ground truth knowledge. IEEE Transactions on Geoscience and Remote Sensing, 43, 857–873. doi:10.1109/TGRS.2004.843074
  • Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., & Blonda, P. (2010a). 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- Part I: System design and implementation. IEEE Transactions on Geoscience and Remote Sensing, 48, 1299–1325. doi:10.1109/TGRS.2009.2032457
  • Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., & Blonda, P. (2010b). “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- Part II: Classification accuracy assessment,” IEEE Trans. IEEE Transactions on Geoscience and Remote Sensing, 48, 1326–1354. doi:10.1109/TGRS.2009.2032064
  • Baraldi, A., Gironda, M., & Simonetti, D. (2010c). Operational two-stage stratified topographic correction of spaceborne multispectral imagery employing an automatic spectral-rule-based decision-tree preliminary classifier. IEEE Transactions Geoscience Remote Sensing, 48(1), 112–146. doi:10.1109/TGRS.2009.2028017
  • Baraldi, A. (2015). “Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images – AutoCloud+,” Invitation to tender ESA/AO/1-8373/15/I-NB – “VAE: Next Generation EO-based Information Services”, doi: 10.13140/RG.2.2.34162.71363. arXiv: 1701.04256. Retrieved Jan., 8, 2017 from https://arxiv.org/ftp/arxiv/papers/1701/1701.04256.pdf
  • Baraldi, A. (2015). “Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images – AutoCloud+,” Invitation to tender ESA/AO/1-8373/15/I-NB – “VAE: Next Generation EO-based Information Services”, doi: 10.13140/RG.2.2.34162.71363. arXiv: 1701.04256. Retrieved Jan., 8, 2017 from https://arxiv.org/ftp/arxiv/papers/1701/1701.04256.pdf
  • Baraldi, A., & Humber, M. (2015). 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 1 – Theory. IEEE Journal of Selected Topics Applications Earth Observation Remote Sensing, 8(3), 1307–1329.
  • Baraldi, A., Humber, M., & Boschetti, L. (2013). 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 Sensing, 5, 5209–5264. doi:10.3390/rs5105209
  • Baraldi, A., Puzzolo, V., Blonda, P., Bruzzone, L., & Tarantino, C. (2006). Automatic spectral rule-based preliminary mapping of calibrated Landsat TM and ETM+ images. IEEE Transactions on Geoscience and Remote Sensing, 44, 2563–2586. doi:10.1109/TGRS.2006.874140
  • Baraldi, A., & Soares, J. V. B. (2017). Multi-objective software suite of two-dimensional shape descriptors for object-based image analysis, subjects: computer vision and pattern recognition (cs.CV). arXiv:1701.01941. Retrieved January, 8, 2017 from https://arxiv.org/ftp/arxiv/papers/1701/1701.01941.pdf
  • Baraldi, A., Tiede, D., Sudmanns, M., Belgiu, M., & Lang, S. (2016, September 14–16). Automated near real-time Earth observation Level 2 product generation for semantic querying. GEOBIA. Enschede, The Netherlands: University of Twente Faculty of Geo-Information and Earth Observation (ITC).
  • Baraldi, A., Wassenaar, T., & Kay, S. (2010d). Operational performance of an automatic preliminary spectral rule-based decision-tree classifier of spaceborne very high resolution optical images. IEEE Transactions on Geoscience and Remote Sensing, 48, 3482–3502. doi:10.1109/TGRS.2010.2046741
  • Beauchemin, M., & Thomson, K. (1997). The evaluation of segmentation results and the overlapping area matrix. International Journal of Remote Sensing, 18, 3895–3899. doi:10.1080/014311697216720
  • Belward, A. (Ed.). (1996). The IGBP-DIS global 1Km land cover data set “DISCover”: Proposal and implementation plans. IGBP-DIS working paper 13. Ispra, Varese, Italy: International Geosphere Biosphere Programme. European Commission Joint Research Center.
  • Benavente, R., Vanrell, M., & Baldrich, R. (2008). Parametric fuzzy sets for automatic color naming. Journal of the Optical Society of America A, 25, 2582–2593. doi:10.1364/JOSAA.25.002582
  • Berlin, B., & Kay, P. (1969). Basic color terms: Their universality and evolution. Berkeley: University of California.
  • Bernus, P., & Noran, O. (2017). Data rich – but information poor. In L. Camarinha-Matos, H. Afsarmanesh, & R. Fornasiero (Eds), Collaboration in a Data-Rich World: PRO-VE 2017 (Vol. 506, pp. 206–214). IFIP Advances in Information and Communication Technology.
  • Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, UK: Clarendon.
  • Bishop, M. P., & Colby, J. D. (2002). Anisotropic reflectance correction of SPOT-3 HRV imagery. International Journal of Remote Sensing, 23(10), 2125–2131. doi:10.1080/01431160110097231
  • Bishop, M. P., Shroder, J. F., & Colby, J. D. (2003). Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology, 55(1–4), 345–361. doi:10.1016/S0169-555X(03)00149-1
  • Bishr, Y. (1998). Overcoming the semantic and other barriers to GIS interoperability. International Journal of Geographical Information Science, 12, 299–314. doi:10.1080/136588198241806
  • Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., … Tiede, D. (2014). Geographic object-based image analysis - towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191. doi:10.1016/j.isprsjprs.2013.09.014
  • Boschetti, L., Flasse, S. P., & Brivio, P. A. (2004). Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto boundary. Remote Sensing of Environment, 91, 280–292. doi:10.1016/j.rse.2004.02.015
  • Boschetti, L., Roy, D. P., Justice, C. O., & Humber, M. L. (2015). MODIS–Landsat fusion for large area 30 m burned area mapping. Remote Sensing of Environment, 161, 27–42. doi:10.1016/j.rse.2015.01.022
  • Bossard, M., Feranec, J., & Otahel, J. (2000). CORINE land cover technical guide – Addendum 2000 (Technical report No 40). European Environment Agency.
  • Capurro, R., & Hjørland, B. (2003). The concept of information. Annual Review of Information Science and Technology, 37, 343–411. doi:10.1002/aris.1440370109
  • Chavez, P. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24, 459–479. doi:10.1016/0034-4257(88)90019-3
  • Cherkassky, V., & Mulier, F. (1998). Learning from data: Concepts, theory, and methods. New York, NY: Wiley.
  • Cimpoi, M., Maji, S., Kokkinos, I., & Vedaldi, A. (2014). Deep filter banks for texture recognition, description, and segmentation. CoRR. abs/1411.6836.
  • CNES. (2015). Venμs satellite sensor level 2 product. Retrieved January, 5, 2016 from https://venus.cnes.fr/en/VENUS/prod_l2.htm
  • Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data. Boca Raton, FL: Lewis Publishers.
  • Couclelis, H. (2010). Ontologies of geographic information. International Journal of Geographical Information Science, 24(12), 1785–1809. doi:10.1080/13658816.2010.484392
  • D‘Elia, S. (2012). Personal communication. European Space Agency.
  • Despini, F., Teggi, S., & Baraldi, A. (2014, September 22). Methods and metrics for the assessment of pan-sharpening algorithms. In L. Bruzzone, J. A. Benediktsson, & F. Bovolo, (Eds.), SPIE Proceedings, Vol. 9244: Image and Signal Processing for Remote Sensing XX. Amsterdam, Netherlands.
  • Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) and VEGA Technologies. (2011). Sentinel-2 MSI – level 2A products algorithm theoretical basis document (Document S2PAD-ATBD-0001). European Space Agency.
  • DiCarlo, J. (2017). The science of natural intelligence: Reverse engineering primate visual perception. Keynote, CVPR17 Conference. Retrieved January, 30, 2018 from https://www.youtube.com/watch?v=ilbbVkIhMgo
  • Di Gregorio, A., & Jansen, L. (2000). Land Cover Classification System (LCCS): Classification concepts and user manual. Rome, Italy: FAO, FAO Corporate Document Repository. Retrieved February, 10, 2015 from, http://www.fao.org/DOCREP/003/X0596E/X0596e00.htm
  • DigitalGlobe (2016). Automated Land Cover Classification. Retrieved November, 13, 2016 from http://gbdxdocs.digitalglobe.com/docs/automated-land-cover-classification
  • Dillencourt, M. B., Samet, H., & Tamminen, M. (1992). A general approach to connected component labeling for arbitrary image representations. Journal of the ACM, 39, 253–280. doi:10.1145/128749.128750
  • Dorigo, W., Richter, R., Baret, F., Bamler, R., & Wagner, W. (2009). Enhanced automated canopy characterization from hyperspectral data by a novel two step radiative transfer model inversion approach. Remote Sensing, 1, 1139–1170. doi:10.3390/rs1041139
  • Duke Center for Instructional Technology. (2016). Measurement: Process and outcome indicators. Retrieved June, 20, 2016 from http://patientsafetyed.duhs.duke.edu/module_a/measurement/measurement.html
  • Dumitru, C. O., Cui, S., Schwarz, G., & Datcu, M. (2015). Information content of very-high-resolution SAR images: Semantics, geospatial context, and ontologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4), 1635–1650. doi:10.1109/JSTARS.2014.2363595
  • Elkan, C. (2003). Using the triangle inequality to accelerate k-means. International Conference Machine Learning.
  • Environmental Protection Agency (EPA). (2007). “Definitions” in Multi-Resolution Land Characteristics Consortium (MRLC). Retrieved November, 13, 2013 from http://www.epa.gov/mrlc/definitions.html#2001
  • Environmental Protection Agency (EPA). (2013). Western ecology division. Retrieved November, 13, 2013 from http://www.epa.gov/wed/pages/ecoregions.htm
  • Etzioni, O. (2017). “What shortcomings do you see with deep learning?” Quora. Retrieved January, 8, 2018 from https://www.quora.com/What-shortcomings-do-you-see-with-deep-learning
  • European Space Agency (ESA). (2015). Sentinel-2 user handbook, standard document, issue 1 rev 2.
  • Feldman, J. (2013). The neural binding problem(s). Cognition Neurodynamic, 7, 1–11. doi:10.1007/s11571-012-9219-8
  • Feng, -C.-C., & Flewelling, D. M. (2004). Assessment of semantic similarity between land use/land cover classification systems. Computers, Environment and Urban Systems, 28, 229–246. doi:10.1016/S0198-9715(03)00020-6
  • Foga, S., Scaramuzza, P., Guo, S., Zhu, Z., Dilley, R., Jr, Beckmann, T., … Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379–390. doi:10.1016/j.rse.2017.03.026
  • Fonseca, F., Egenhofer, M., Agouris, P., & Camara, G. (2002). Using ontologies for integrated geographic information systems. Transactions in GIS, 6, 231–257. doi:10.1111/1467-9671.00109
  • Fowler, M. (2003). UML Distilled (3rd ed.). Boston, MS: Addison-Wesley.
  • Frintrop, S. (2011). Computational visual attention. In A. A. Salah & T. Gevers (Eds.), Computer analysis of human behavior, advances in pattern recognition. Springer.
  • Fritzke, B. (1997a). Some competitive learning methods. Retrieved March, 17, 2015 from http://www.demogng.de/JavaPaper/t.html
  • Fritzke, B. (1997b). The LBG-U method for vector quantization - An improvement over LBG inspired from neural networks. Neural Processing Letters, 5(1), 35–45. doi:10.1023/A:1009653226428
  • GeoTerraImage. (2015). Provincial and national land cover 30m. Retrieved September, 22, 2015 from http://www.geoterraimage.com/productslandcover.php
  • Gevers, T., Gijsenij, A., van de Weijer, J., & Geusebroek, J. M. (2012). Color in computer vision. Hoboken, NJ: Wiley.
  • Ghahramani, Z. (2011). Bayesian nonparametrics and the probabilistic approach to modelling. Philosophical Transactions R Social A, 1–27.
  • Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., … Plaza, A. (2017). Advances in hyperspectral image and signal processing. IEEE Geoscience Remote Sensing Magazine, 12(37), 78.
  • Griffin, L. D. (2006). Optimality of the basic colour categories for classification. Journal of the Royal Society Interface, 3, 71–85. doi:10.1098/rsif.2005.0076
  • Griffith, G. E., & Omernik, J. M. (2009). Ecoregions of the United States-Level III (EPA). In C. J. Cleveland (Ed.), Encyclopedia of earth. Washington, DC: Environmental Information Coalition, National Council for Science and the Environment.
  • Group on Earth Observation (GEO). (2005). The Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan, adopted 16 February 2005. Retrieved January, 10, 2012 from http://www.earthobservations.org/docs/10-Year%20Implementation%20Plan.pdf
  • Group on Earth Observation/Committee on Earth Observation Satellites (GEO-CEOS). (2010). A quality assurance framework for earth observation, version 4.0. Retrieved November, 15, 2012 from http://qa4eo.org/docs/QA4EO_Principles_v4.0.pdf
  • Group on Earth Observation/Committee on Earth Observation Satellites (GEO-CEOS) - Working Group on Calibration and Validation (WGCV). (2015). Land Product Validation (LPV). Retrieved March, 20, 2015 from http://lpvs.gsfc.nasa.gov/
  • Guarino, N. (1995). Formal ontology, conceptual analysis and knowledge representation. International Journal of Human-Computer Studies, 43, 625–640. doi:10.1006/ijhc.1995.1066
  • Gutman, G., Janetos, A. C., Justice, C. O., Moran, E. F., Mustard, J. F., Rindfuss, R. R., … Cochrane, M. A. (Eds.). (2004). Land change science. Dordrecht, The Netherlands: Kluwer.
  • Hadamard, J. (1902). Sur les problemes aux derivees partielles et leur signification physique. Princet University Bulletin, 13, 49–52.
  • Homer, C., Huang, C. Q., Yang, L. M., Wylie, B., & Coan, M. (2004). Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering & Remote Sensing, 70, 829–840. doi:10.14358/PERS.70.7.829
  • Hunt, N., & Tyrrell, S. (2012). Stratified sampling. Coventry University. Retrieved February, 7, 2012 from http://www.coventry.ac.uk/ec/~nhunt/meths/strati.html
  • IBM. (2016). The four V’s of big data, IBM big data & analytics hub. Retrieved January, 30, 2018 from http://www.ibmbigdatahub.com/infographic/four-vs-big-data
  • Julesz, B. (1986). Texton gradients: The texton theory revisited. In Biomedical and life sciences collection (Vol. 54, pp.4–5). Berlin/Heidelberg: Springer.
  • Julesz, B., Gilbert, E. N., Shepp, L. A., & Frisch, H. L. (1973). Inability of humans to discriminate between visual textures that agree in second-order statistics – Revisited. Perception, 2, 391–405. doi:10.1068/p020391
  • Kavouras, M., & Kokla, M. (2002). A method for the formalization and integration of geographical categorizations. International Journal of Geographical Information Science, 16, 439–453. doi:10.1080/13658810210129120
  • Kuzera, K., & Pontius, R. G., Jr. (2008). Importance of matrix construction for multiple-resolution categorical map comparison. GIScience & Remote Sensing, 45, 249–274. doi:10.2747/1548-1603.45.3.249
  • Laurini, R., & Thompson, D. (1992). Fundamentals of spatial information systems. London, UK: Academic Press.
  • Lee, D., Baek, S., & Sung, K. (1997). Modified k-means algorithm for vector quantizer design. IEEE Signal Processing Letters, 4, 2–4. doi:10.1109/97.551685
  • Liang, S. (2004). Quantitative remote sensing of land surfaces. Hoboken, NJ: John Wiley and Sons.
  • Lillesand, T., & Kiefer, R. (1979). Remote sensing and image interpretation. New York, NY: John Wiley and Sons.
  • Linde, Y., Buzo, A., & Gray, R. M. (1980). An algorithm for vector quantizer design. IEEE Transactions Communicable, 28, 84–95. doi:10.1109/TCOM.1980.1094577
  • Lipson, H. (2007). Principles of modularity, regularity, and hierarchy for scalable systems. Journal of Biological Physics and Chemistry, 7, 125–128. doi:10.4024/40701.jbpc.07.04
  • Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. doi:10.1109/TIT.1982.1056489
  • Lück, W., & van Niekerk, A. (2016). Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images. International Journal of Applied Earth Observation and Geoinformation, 47, 1–14. doi:10.1016/j.jag.2015.11.019
  • Lunetta, R., & Elvidge, D. (1999). Remote sensing change detection: Environmental monitoring methods and applications. London, UK: Taylor & Francis.
  • Luo, Y., Trishchenko, A. P., & Khlopenkov, K. V. (2008). Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sensing of Environment, 112, 4167–4185. doi:10.1016/j.rse.2008.06.010
  • Mallat, S. (2016). Understanding deep convolutional networks. Philosophical Transactions R Social A, 374, 1–16. doi:10.1098/rsta.2015.0203
  • Marcus, G. (2018). Deep learning: A critical appraisal. arXiv: 1801.00631. Retrieved January, 16, 2018 from https://arxiv.org/ftp/arxiv/papers/1801/1801.00631.pdf
  • Marr, D. (1982). Vision. New York, NY: Freeman and C.
  • Martinetz, T., Berkovich, G., & Schulten, K. (1994). Topology representing networks. Neural Networks, 7(3), 507–522. doi:10.1016/0893-6080(94)90109-0
  • Mather, P. (1994). Computer processing of remotely-sensed images - an introduction. Hoboken, NJ: Wiley.
  • Matsuyama, T., & Hwang, V. S. (1990). SIGMA – A knowledge-based aerial image understanding system. New York, NY: Plenum Press.
  • Mazzuccato, M., & Robinson, D. (2017). Market creation and the European space agency (European Space Agency (ESA) Report).
  • Memarsadeghi, N., Mount, D., Netanyahu, N., & Le Moigne, J. (2007). A fast implementation of the ISODATA clustering algorithm. International Journal of Computational Geometry & Applications, 17(1), 71–103. doi:10.1142/S0218195907002252
  • Mizen, H., Dolbear, C., & Hart, G. (2005, November 29–30). Ontology ontogeny: Understanding how an ontology is created and developed. In M. Rodriguez, I. Cruz, S. Levashkin, & M. J. Egenhofer (Eds.) Proceedings GeoSpatial Semantics: First International Conference, GeoS 2005 (pp. 15–29). Mexico City, Mexico, Springer Berlin Heidelberg.
  • Muirhead, K., & Malkawi, O. (1989, Sepptember 5–8). Automatic classification of AVHRR images. In Proceedings Fourth AVHRR Data Users Meeting (pp. 31–34). Rottenburg, Germany.
  • Nagao, M., & Matsuyama, T. (1980). A structural analysis of complex aerial photographs. New York, NY: Plenum.
  • National Aeronautics and Space Administration (NASA). (2016a). Getting petabytes to people: How the EOSDIS facilitates earth observing data discovery and use. [Online]. Retrieved December, 29, 2016 from https://earthdata.nasa.gov/getting-petabytes-to-people-how-the-eosdis-facilitates-earth-observing-data-discovery-and-use
  • National Aeronautics and Space Administration (NASA). (2016b). Data processing levels. [Online]. Retrieved December, 20, 2016 from https://science.nasa.gov/earth-science/earth-science-data/data-processing-levels-for-eosdis-data-products
  • Open Geospatial Consortium (OGC) Inc. (2015). OpenGIS® implementation standard for geographic information - simple feature access - Part 1: Common architecture. Retrieved March, 8, 2015 from http://www.opengeospatial.org/standards/is
  • Ortiz, A., & Oliver, G. (2006). On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures. Pattern Recognition Letters, 27, 1916–1926. doi:10.1016/j.patrec.2006.05.002
  • Parisi, D. (1991). La scienza cognitive tra intelligenza artificiale e vita artificiale. In Neurosceinze e Scienze dell’Artificiale: Dal Neurone all’Intelligenza. Bologna, Italy: Patron Editore.
  • Patané, G., & Russo, M. (2001). The enhanced-LBG algorithm. Neural Networks, 14(9), 1219–1237. doi:10.1016/S0893-6080(01)00104-6
  • Patané, G., & Russo, M. (2002). Fully automatic clustering system. IEEE Transactions on Neural Networks, 13(6), 1285–1298. doi:10.1109/TNN.2002.804226
  • Pearl, J. (2009). Causality: Models, reasoning and inference. New York, NY: Cambridge University Press.
  • Piaget, J. (1970). Genetic epistemology. New York, NY: Columbia University Press.
  • Pontius, R. G., Jr., & Connors, J. (2006, July 5–7). “Expanding the conceptual, mathematical and practical methods for map comparison. In M. Caetano & M. Painho (Eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences (pp. 64–79). Lisbon. Instituto Geográfico Português.
  • Pontius, R. G., Jr., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. doi:10.1080/01431161.2011.552923
  • Riano, D., Chuvieco, E., Salas, J., & Aguado, I. (2003). Assessment of different topographic corrections in landsat-TM data for mapping vegetation types (2003). IEEE Transactions on Geoscience and Remote Sensing, 41(5), 1056–1061. doi:10.1109/TGRS.2003.811693
  • Richter, R., & Schläpfer, D. (2012a). Atmospheric/topographic correction for satellite imagery – ATCOR-2/3 user guide, version 8.2 BETA. Retrieved April, 12, 2013 from http://www.dlr.de/eoc/Portaldata/60/Resources/dokumente/5_tech_mod/atcor3_manual_2012.pdf
  • Richter, R., & Schläpfer, D. (2012b). Atmospheric/Topographic correction for airborne imagery – ATCOR-4 User Guide, Version 6.2 BETA, 2012. Retrieved April, 12, 2013 from http://www.dlr.de/eoc/Portaldata/60/Resources/dokumente/5_tech_mod/atcor4_manual_2012.pdf
  • Roy, D. P., Ju, J., Kline, K., Scaramuzza, P. L., Kovalskyy, V., Hansen, M., … Zhang, C. (2010). Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, 114, 35–49. doi:10.1016/j.rse.2009.08.011
  • Salmon, B., Wessels, K., van den Bergh, F., Steenkamp, K., Kleynhans, W., Swanepoel, D., … Kovalskyy, V. (2013, July 21–26). Evaluation of rule-based classifier for Landsat-based automated land cover mapping in South Africa. IEEE International Geoscience Remote Sensing Symposium (IGARSS) (pp. 4301–4304).
  • Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S., & Martonchik, J. V. (2006). Reflectance quantities in optical remote sensing - Definitions and case studies. Remote Sensing of Environment, 103, 27–42. doi:10.1016/j.rse.2006.03.002
  • Schläpfer, D., Richter, R., & Hueni, A. (2009). Recent developments in operational atmospheric and radiometric correction of hyperspectral imagery, in Proc. 6th EARSeL SIG IS Workshop, 16-19 March 2009. Retrieved July, 14, 2012 from http://www.earsel6th.tau.ac.il/~earsel6/CD/PDF/earsel-PROCEEDINGS/3054%20Schl%20pfer.pdf
  • Shackelford, K., & Davis, C. H. (2003a). A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(10), 2354–2364. doi:10.1109/TGRS.2003.815972
  • Shackelford, K., & Davis, C. H. (2003b). A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1920–1932. doi:10.1109/TGRS.2003.814627
  • Sheskin, D. (2000). Handbook of parametric and nonparametric statistical procedures. Boca Raton, FL: Chapman & Hall/CRC.
  • Shotton, J., Winn, J., Rother, C., & Criminisi, A. (2009). Texton-boost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision, 81(1), 2–23. doi:10.1007/s11263-007-0109-1
  • Simonetti, D., Marelli, A., & Eva, H. (2015b). Impact toolbox. (JRC Technical EUR 27358 EN).
  • Simonetti, D., Simonetti, E., Szantoi, Z., Lupi, A., & Eva, H. D. (2015a). First results from the phenology based synthesis classifier using Landsat-8 imagery. IEEE Geoscience and Remote Sensing Letters, 12(7), 1496–1500. doi:10.1109/LGRS.2015.2409982
  • Smeulders, A., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions Pattern Analysis Machine Intelligent, 22(12), 1349–1380. doi:10.1109/34.895972
  • Soares, J., Baraldi, A., & Jacobs, D. (2014). Segment-based simple-connectivity measure design and implementation (Tech. Rep). College Park, MD: University of Maryland.
  • Sonka, M., Hlavac, V., & Boyle, R. (1994). Image processing, analysis and machine vision. London, UK: Chapman & Hall.
  • Sowa, J. F. (2000). Knowledge representation: Logical, Philosophical, and Computational Foundations. Pacific Grove, CA: Brooks/Cole.
  • Stehman, S. V. (1999). Comparing thematic maps based on map value. International Journal of Remote Sensing, 20, 2347–2366. doi:10.1080/014311699212065
  • Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64, 331–344. doi:10.1016/S0034-4257(98)00010-8
  • Swain, P. H., & Davis, S. M. (1978). Remote sensing: The quantitative approach. New York, NY: McGraw-Hill.
  • Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M., & Lang, S. (2016, March 15–17). ImageQuerying (IQ) – Earth observation image content extraction & querying across time and space. Submitted (Oral presentation and poster session), ESA 2016 Conf. on Big Data From Space, BIDS ’16. Santa Cruz de Tenerife, Spain.
  • Trimble. (2015). eCognition® Developer 9.0 Reference Book.
  • Tsotsos, J. K. (1990). Analyzing vision at the complexity level. Behavioral and Brain Sciences, 13, 423–445. doi:10.1017/S0140525X00079577
  • Vermote, E., & Saleous, N. (2007). LEDAPS surface reflectance product description - Version 2.0. University of Maryland at College Park/Dept Geography and NASA/GSFC Code 614.5.
  • Vogelmann, J. E., Howard, S. M., Yang, L., Larson, C. R., Wylie, B. K., & van Driel, N. (2001). Completion of the 1990s National Land Cover Data set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photo Engineering Remote Sensing, 67, 650–662.
  • Vogelmann, J. E., Sohl, T. L., Campbell, P. V., & Shaw, D. M. (1998). Regional land cover characterization using Landsat Thematic Mapper data and ancillary data sources. Environmental Monitoring and Assessment, 51, 415–428. doi:10.1023/A:1005996900217
  • Web-Enabled Landsat Data (WELD) Tile FTP. 2015. Retrieved December, 12, 2016 from https://weld.cr.usgs.gov/
  • Wenwen, L. M., Goodchild, F., & Church, R. L. (2013). An efficient measure of compactness for 2D shapes and its application in regionalization problems. International Journal of Geographical Info Science, 1–24.
  • Wickham, J. D., Stehman, S. V., Fry, J. A., Smith, J. H., & Homer, C. G. (2010). Thematic accuracy of the NLCD 2001 land cover for the conterminous United States. Remote Sensing of Environment, 114, 1286–1296. doi:10.1016/j.rse.2010.01.018
  • Wickham, J. D., Stehman, S. V., Gass, L., Dewitz, J., Fry, J. A., & Wade, T. G. (2013). Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sensing of Environment, 130, 294–304. doi:10.1016/j.rse.2012.12.001
  • Wikipedia. (2018a). Big data. Retrieved January, 30, 2018 from en.wikipedia.org/wiki/Big_data
  • Wikipedia. (2018b). Latent variable. Retrieved January, 30, 2018 from https://en.wikipedia.org/wiki/Latent_variable
  • Wikipedia. (2018c). Bayesian inference. Retrieved January, 30, 2018 from https://en.wikipedia.org/wiki/Bayesian_inference
  • Xian, G., & Homer, C. (2010). Updating the 2001 National Land Cover Database impervious surface products to 2006 using Landsat imagery change detection methods. Remote Sensing of Environment, 114, 1676–1686. doi:10.1016/j.rse.2010.02.018
  • Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. doi:10.1080/17538947.2016.1239771
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353. doi:10.1016/S0019-9958(65)90241-X