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Data and Information Quality in Remote Sensing

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Information Quality in Information Fusion and Decision Making

Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

Remote sensing datasets are characterized by multiple types of imperfections that alter extracted information and taken decisions to a variable degree depending on data acquisition conditions, processing, and final product requirements. Therefore, regardless of the sensors, type of data, extracted information, and complementary algorithms, the quality assessment question is a pervading and particularly complex one. This chapter summarizes relevant quality assessment approaches that have been proposed for data acquisition, information extraction, and data and information fusion, of the remote sensing acquisition-decision process. The case of quality evaluation for geographic information systems, which make use of remote sensing products, is also described. Aspects of a comprehensive quality model for remote sensing and problems that remain to be addressed offer a perspective of possible evolutions in the field.

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Notes

  1. 1.

    https://earthdata.nasa.gov/user-resources/remote-sensors.

References

  1. J.B. Campbell, R.H. Wynne, Introduction to Remote Sensing (Guilford Press, New York, 2011)

    Google Scholar 

  2. Y. Ma, H. Wu, L. Wang, B. Huang, R. Ranjan, A. Zomaya, W. Jie, Remote sensing big data computing: Challenges and opportunities. Futur. Gener. Comput. Syst. 51, 47–60 (2015)

    Article  Google Scholar 

  3. T. Miyoshi, K. Kondo, K. Terasaki, Big ensemble data assimilation in numerical weather prediction. Computer 11, 15–21 (2015)

    Article  Google Scholar 

  4. T. Miyoshi, M. Kunii, J. Ruiz, G.Y. Lien, S. Satoh, T. Ushio, et al., Big data assimilation – revolutionizing severe weather prediction. Bull. Am. Meteorol. Soc. 97(8), 1347–1354 (2016)

    Article  Google Scholar 

  5. E. Osuteye, C. Johnson, D. Brown, The data gap: An analysis of data availability on disaster losses in sub-Saharan African cities. Int. J. Disaster Risk Reduction 26, 24–33 (2017)

    Article  Google Scholar 

  6. C. Senf, R. Seidl, P. Hostert, Remote sensing of forest insect disturbances: Current state and future directions. Int. J. Appl. Earth Obs. Geoinf. 60, 49–60 (2017)

    Article  Google Scholar 

  7. S. Li, S. Dragicevic, F.A. Castro, M. Sester, S. Winter, A. Coltekin, et al., Geospatial big data handling theory and methods: A review and research challenges. ISPRS J. Photogramm. Remote Sens. 115, 119–133 (2016)

    Article  Google Scholar 

  8. L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, Remote Sensing Image Fusion (CRC Press, Boca Raton, 2015)

    Book  Google Scholar 

  9. L. Gómez-Chova, D. Tuia, G. Moser, G. Camps-Valls, Multimodal classification of remote sensing images: A review and future directions. Proc. IEEE 103(9), 1560–1584 (2015)

    Article  Google Scholar 

  10. J.A. Cummings, Ocean data quality control, in Operational Oceanography in the 21st Century, (Springer, Dordrecht, 2011), pp. 91–121

    Google Scholar 

  11. K.A. Kilpatrick, G. Podestá, S. Walsh, E. Williams, V. Halliwell, et al., A decade of sea surface temperature from MODIS. Remote Sens. Environ. 165, 27–41 (2012)

    Article  Google Scholar 

  12. H. Uehara, A.A. Kruts, Y.N. Volkov, T. Nakamura, T. Ono, H. Mitsudra, A new climatology of the Okhotsk sea derived from the FERHRI database. J. Oceanogr. 68(6), 869–886 (2012)

    Article  Google Scholar 

  13. F. Xu, A. Ignatov, In situ SST quality monitor (i quam). J. Atmos. Ocean. Technol. 31(1), 164–180 (2014)

    Article  Google Scholar 

  14. C. Donlon, I. Robinson, K.S. Casey, J. Vazquez-Cuervo, E. Armstrong, O. Arino, et al., The global ocean data assimilation experiment high-resolution sea surface temperature pilot project. Bull. Am. Meteorol. Soc. 88(8), 1197–1214 (2007)

    Article  Google Scholar 

  15. S. Guinehut, C. Coatanoan, A.L. Dhomps, P.Y. Le Traon, G. Larnicol, On the use of satellite altimeter data in Argo quality control. J. Atmos. Ocean. Technol. 26(2), 395–402 (2009)

    Article  Google Scholar 

  16. A.S. Bogdanoff, D.L. Westphal, J.R. Campbell, J.A. Cummings, E.J. Hyer, J.S. Reid, C.A. Clayson, Sensitivity of infrared sea surface temperature retrievals to the vertical distribution of airborne dust aerosol. Remote Sens. Environ. 159, 1–13 (2015)

    Article  Google Scholar 

  17. C.J. Merchant, A.R. Harris, E. Maturi, S. MacCallum, Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval. Q. J. R. Meteorol. Soc. 131(611), 2735–2755 (2005)

    Article  Google Scholar 

  18. B.B. Barnes, C. Hu, A hybrid cloud detection algorithm to improve MODIS sea surface temperature data quality and coverage over the eastern gulf of Mexico. IEEE Trans. Geosci. Remote Sens. 51(6), 3273–3285 (2013)

    Article  Google Scholar 

  19. M. Bouali, A. Ignatov, Adaptive reduction of striping for improved sea surface temperature imagery from Suomi national polar-orbiting partnership (S-NPP) visible infrared imaging radiometer suite (viirs). J. Atmos. Ocean. Technol. 31(1), 150–163 (2014)

    Article  Google Scholar 

  20. P.K. Koner, A. Harris, E. Maturi, Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage. Remote Sens. Environ. 174, 266–278 (2016)

    Article  Google Scholar 

  21. J.A. Cummings, Operational multivariate ocean data assimilation. Q. J. R. Meteorol. Soc. 131(613), 3583–3604 (2005)

    Article  Google Scholar 

  22. JCOMM Data Management Coordination. Final report of the third session of the JCOMM data management coordination group (jcomm dmcg-iii), Tech. Rep. 56, Intergovernmental Oceanographic Commission of UNESCO and World Meteorological Organization (2008)

    Google Scholar 

  23. E. Ahokas, H. Kaartinen, J. Hyyppä, A quality assessment of airborne laser scanner data. Int. Arch. Photogramm. Remote Sens. 34(part3), W13 (2003)

    Google Scholar 

  24. Z. Wan, Y. Zhang, Q. Zhang, Z.L. Li, Quality assessment and validation of the MODIS global land surface temperature. Int. J. Remote Sens. 25(1), 261–274 (2004)

    Article  Google Scholar 

  25. M. Neubert, H. Herold, G. Meinel, Evaluation of remote sensing image~segmentation quality–further results and concepts.~Int.~Arch.~Photogramm.~Remote~Sens.~Spatial Inf. Sci. 36(4/C42) (2006). http://www.isprs.org/proceedings/XXXVI/4-C42/Papers/10_Adaption%20and%20further%20development%20II/OBIA2006_Neubert_Herold_Meinel.pdf

  26. R.R. Colditz, C. Conrad, T. Wehrmann, M. Schmidt, S. Dech, TiSeG: A flexible software tool for time-series generation of MODIS data utilizing the quality assessment science data set. IEEE Trans. Geosci. Remote Sens. 46(10), 3296–3308 (2008)

    Article  Google Scholar 

  27. K.A. Razak, M.W. Straatsma, C.J. Van Westen, J.P. Malet, S.M. De Jong, Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization. Geomorphology 126(1–2), 186–200 (2011)

    Article  Google Scholar 

  28. Q. Zhan, M. Molenaar, K. Tempfli, W. Shi, Quality assessment for geo-spatial objects derived from remotely sensed data. Int. J. Remote Sens. 26(14), 2953–2974 (2005)

    Article  Google Scholar 

  29. Y. Shuai, C.B. Schaaf, A.H. Strahler, J. Liu, Z. Jiao, Quality assessment of BRDF/albedo retrievals in MODIS operational system. Geophys. Res. Lett. 35(L05407), 5 p (2008)

    Google Scholar 

  30. V.E. Brando, J.M. Anstee, M. Wettle, A.G. Dekker, S.R. Phinn, C. Roelfsema, A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sens. Environ. 113(4), 755–770 (2009)

    Article  Google Scholar 

  31. H.J. Buiten, B. Van Putten, Quality assessment of remote sensing image registration-analysis and testing of control point residuals. ISPRS J. Photogramm. Remote Sens. 52(2), 57–73 (1997)

    Article  Google Scholar 

  32. T.R. Loveland, B.C. Reed, J.F. Brown, D.O. Ohlen, Z. Zhu, L.W.M.J. Yang, J.W. Merchant, Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 21(6–7), 1303–1330 (2000)

    Article  Google Scholar 

  33. U. Weidner, Contribution to the assessment of segmentation quality for remote sensing applications. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 37(B7), 479–484 (2008)

    Google Scholar 

  34. M.J. Smith, J. Rose, S. Booth, Geomorphological mapping of glacial landforms from remotely sensed data: An evaluation of the principal data sources and an assessment of their quality. Geomorphology 76(1–2), 148–165 (2006)

    Article  Google Scholar 

  35. S.O. Elberink, G. Vosselman, Quality analysis on 3D building models reconstructed from airborne laser scanning data. ISPRS J. Photogramm. Remote Sens. 66(2), 157–165 (2011)

    Article  Google Scholar 

  36. G.M. Foody, Harshness in image classification accuracy assessment. Int. J. Remote Sens. 29(11), 3137–3158 (2008)

    Article  Google Scholar 

  37. P.C. Smits, S.G. Dellepiane, R.A. Schowengerdt, Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach. Int. J. Remote Sens. 20(8), 1461–1486 (1999)

    Article  Google Scholar 

  38. Y. Ke, L.J. Quackenbush, J. Im, Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sens. Environ. 114(6), 1141–1154 (2010)

    Article  Google Scholar 

  39. R.D. Fiete, T.A. Tantalo, Comparison of SNR image quality metrics for remote sensing systems. Opt. Eng. 40(4), 574–586 (2001)

    Article  Google Scholar 

  40. R.D. Fiete, T.A. Tantalo, J.R. Calus, J.A. Mooney, Image quality of sparse aperture designs for remote sensing. Opt. Eng. 41(8), 1957–1970 (2002)

    Article  Google Scholar 

  41. D. Scherler, S. Leprince, M.R. Strecker, Glacier-surface velocities in alpine terrain from optical satellite imagery – Accuracy improvement and quality assessment. Remote Sens. Environ. 112(10), 3806–3819 (2008)

    Article  Google Scholar 

  42. Q. Liu, R. Klucik, C. Chen, G. Grant, D. Gallaher, Q. Lv, L. Shang, Unsupervised detection of contextual anomaly in remotely sensed data. Remote Sens. Environ. 202, 75–87 (2017)

    Article  Google Scholar 

  43. J. Li, Spatial quality evaluation of fusion of different resolution images. Int. Arch. Photogramm. Remote Sens. 33(B2; PART 2), 339–346 (2000)

    Google Scholar 

  44. W. Shi, C. Zhu, Y. Tian, J. Nichol, Wavelet-based image fusion and quality assessment. Int. J. Appl. Earth Obs. Geoinf. 6(3–4), 241–251 (2005)

    Article  Google Scholar 

  45. R.G. Congalton, K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (CRC Press, Boca Raton, 2008)

    Book  Google Scholar 

  46. Y. Chen, Z.Y. Xue, R.S. Blum, Theoretical analysis of an information-based quality measure for image fusion. Inf. Fusion 9, 161–175 (2008)

    Article  Google Scholar 

  47. L. Wald, T. Ranchin, M. Mangolini, Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogramm. Eng. Remote. Sens. 63(6), 691–699 (1997)

    Google Scholar 

  48. J. Zhou, D.L. Civco, J.A. Silander, A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int. J. Remote Sens. 19(4), 743–757 (1998)

    Article  Google Scholar 

  49. L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini, M. Selva, Multispectral and panchromatic data fusion assessment without reference. Photogramm. Eng. Remote Sens. 74(2), 193–200 (2008)

    Article  Google Scholar 

  50. M.M. Khan, L. Alparone, J. Chanussot, Pansharpening quality assessment using the modulation transfer functions of instruments. IEEE Trans. Geosci. Remote Sens. 47(11), 3880–3891 (2009)

    Article  Google Scholar 

  51. J. Dong, D. Zhuang, Y. Huang, J. Fu, Advances in multi-sensor data fusion: Algorithms and applications. Sensors 9(10), 7771–7784 (2009)

    Article  Google Scholar 

  52. M. Ehlers, S. Klonus, P. Johan Åstrand, P. Rosso, Multi-sensor image fusion for pansharpening in remote sensing. Int. J. Image Data Fusion 1(1), 25–45 (2010)

    Article  Google Scholar 

  53. W. Wang, F. Chang, A multi-focus image fusion method based on Laplacian pyramid. J. Comput. 6(12), 2559–2566 (2011)

    Article  Google Scholar 

  54. X.X. Zhu, R. Bamler, A sparse image fusion algorithm with application to pan-sharpening. IEEE Trans. Geosci. Remote Sens. 51(5), 2827–2836 (2013)

    Article  Google Scholar 

  55. Y. Jiang, M. Wang, Image fusion with morphological component analysis. Information Fusion 18, 107–118 (2014)

    Article  Google Scholar 

  56. Y. Zhang, R.K. Mishra, From UNB PanSharp to Fuze Go – The success behind the pan-sharpening algorithm. Int. J. Image Data Fusion 5(1), 39–53 (2014)

    Article  Google Scholar 

  57. J. Liu, J. Huang, S. Liu, H. Li, Q. Zhou, J. Liu, Human visual system consistent quality assessment for remote sensing image fusion. ISPRS J. Photogramm. Remote Sens. 105, 79–90 (2015)

    Article  Google Scholar 

  58. P. Jagalingam, A.V. Hegde, A review of quality metrics for fused image. Aquatic Procedia 4, 133–142 (2015)

    Article  Google Scholar 

  59. R.C. Frohn, R.D. Lopez, Remote Sensing for Landscape Ecology. New Metric Indicators: Monitoring, Modeling, and Assessment of Ecosystems (CRC Press, Boca Raton, 2017)

    Google Scholar 

  60. C. Simoonga, J. Utzinger, S. Brooker, P. Vounatsou, C.C. Appleton, A.S. Stensgaard, et al., Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa. Parasitology 136(13), 1683–1693 (2009)

    Article  Google Scholar 

  61. G.S. Bhunia, M.R. Dikhit, S. Kesari, G.C. Sahoo, P. Das, Role of remote sensing, geographical information system (GIS) and bioinformatics in kala-azar epidemiology. J. Biomed. Res. 25(6), 373–384 (2011)

    Article  Google Scholar 

  62. E. Opolot, Application of remote sensing and geographical information systems in flood management: A review. Res. J. Appl. Sci. Eng. Technol. 6(10), 1884–1894 (2013)

    Article  Google Scholar 

  63. D. Oikonomidis, S. Dimogianni, N. Kazakis, K. Voudouris, A GIS/remote sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece. J. Hydrol. 525, 197–208 (2015)

    Article  Google Scholar 

  64. I.R. Hegazy, M.R. Kaloop, Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. Int. J. Sustainable Built Environ. 4(1), 117–124 (2015)

    Article  Google Scholar 

  65. N. Baghdadi, C. Mallet, M. Zribi (eds.), QGIS and Applications in Agriculture and Forest (Wiley, Hoboken, 2018)

    Google Scholar 

  66. R.J. Patil, Spatial Techniques for Soil Erosion Estimation: Remote Sensing and GIS Approach (Springer, Cham, Switzerland, 2018)

    Google Scholar 

  67. N.R. Chrisman, The role of quality information in the long-term functioning of a geographic information system. Cartographica Int. J. Geographic Inf. Geovisualization 21(2–3), 79–88 (1984). Part 2 Issues and problems relating to cartographic data use, exchange and transfer

    Article  Google Scholar 

  68. R. Devillers, A. Stein, Y. Bédard, N. Chrisman, P. Fisher, W. Shi, Thirty years of research on spatial data quality: Achievements, failures, and opportunities. Trans. GIS 14(4), 387–400 (2010)

    Article  Google Scholar 

  69. ISO 8402:1994, Quality management and quality assurance – Vocabulary, https://www.iso.org/standard/20115.html

  70. S. Servigne, N. Lesage, T. Libourel, Quality components, standards, and metadata, in Fundamentals of Spatial Data Quality, (2006), pp. 179–210

    Chapter  Google Scholar 

  71. ISO 19101-1:2014, Geographic information – Reference model – Part 1: Fundamentals, https://www.iso.org/standard/59164.html

  72. ISO 19115-1:2014, Geographic information – Metadata – Part 1: Fundamentals, https://www.iso.org/standard/53798.html

  73. ISO 19101-2:2018, Geographic information – Reference model – Part 2: Imagery, https://www.iso.org/standard/69325.html

  74. ISO 19115:2009, Geographic information – Metadata – Part 2: Extensions for imagery and gridded data, https://www.iso.org/standard/39229.html

  75. ISO/TC 211 Geographic information/Geomatics, https://www.iso.org/committee/54904.html

  76. ISO 19157:2013(en), Geographic information – Data quality, https://www.iso.org/obp/ui/#iso:std:iso:19157:ed-1:v1:en

  77. R. Devillers, R. Jeansoulin, Fundamentals of Spatial Data Quality (Wiley, Hoboken, 2010)

    MATH  Google Scholar 

  78. A. Zargar, R. Devillers, An operation-based communication of spatial data quality. IEEE Int. Conf. Adv. Geogr. Inf. Syst. Web Serv., 140–145 (2009)

    Google Scholar 

  79. P. Díaz, J. Masó, E. Sevillano, M. Ninyerola, A. Zabala, I. Serral, et al., Analysis of quality metadata in the GEOSS clearinghouse. Int. J. Spatial Data Infrastructures Res. 7, 352–377 (2012)

    Google Scholar 

  80. I. Pôças, J. Gonçalves, B. Marcos, J. Alonso, P. Castro, J.P. Honrado, Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring. Int. J. Geogr. Inf. Sci. 28(11), 2356–2371 (2014)

    Article  Google Scholar 

  81. H. Senaratne, A. Mobasheri, A.L. Ali, C. Capineri, M. Haklay, A review of volunteered geographic information quality assessment methods. Int. J. Geogr. Inf. Sci. 31(1), 139–167 (2017)

    Article  Google Scholar 

  82. D.P. Ballou, H.L. Pazer, Modeling data and process quality in multi-input, multi-output information systems. Manag. Sci. 31(2), 150–162 (1985)

    Article  Google Scholar 

  83. R.Y. Wang, D.M. Strong, Beyond accuracy: What data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)

    Article  Google Scholar 

  84. S.E. Madnick, R.Y. Wang, Y.W. Lee, H. Zhu, Overview and framework for data and information quality research. J. Data Inf. Qual. 1(1), article 2, 22 p (2009)

    Google Scholar 

  85. Z. Chen, Data Mining and Uncertain Reasoning: An Integrated Approach (Wiley, New York, 2001)

    Google Scholar 

  86. Naumann F, From databases to information systems-information quality makes the difference. 6th International Conference on Information Quality. (2001) pp. 244–260

    Google Scholar 

  87. A. Klein, W. Lehner, Representing data quality in sensor data streaming environments. J. Data Inf. Qual. 1(2), 10 (2009)

    Google Scholar 

  88. Rogova GL, Bosse E, Information quality in information fusion. 13th IEEE Conference on Information Quality in Information Fusion, (2010) pp. 1–8

    Google Scholar 

  89. I.G. Todoran, L. Lecornu, A. Khenchaf, J.M. Le Caillec, Fusion systems evaluation, in Multisensor Data Fusion: From Algorithms and Architectural Design to Applications, (CRC Press, Boca Raton, USA, 2017), pp. 147–156

    Google Scholar 

  90. V. Gunes, S. Peter, T. Givargis, F. Vahid, A survey on concepts, applications, and challenges in cyber-physical systems. KSII Trans. Int. Inf. Syst. 8(12), 4242–4268 (2014)

    Google Scholar 

  91. N. Chen, C. Xiao, F. Pu, X. Wang, C. Wang, Z. Wang, et al., Cyber-physical geographical information service-enabled control of diverse in-situ sensors. Sensors 15(2), 2565–2592 (2015)

    Article  Google Scholar 

  92. G. Mois, T. Sanislav, S.C. Folea, A cyber-physical system for environmental monitoring. IEEE Trans. Instrum. Meas. 65(6), 1463–1471 (2016)

    Article  Google Scholar 

  93. K. Sha, S. Zeadally, Data quality challenges in cyber-physical systems. J. Data Inf. Qual. 6(2–3), 8 (2015)

    Google Scholar 

  94. P. Merino Laso, D. Brosset, J. Puentes, Monitoring approach of cyber-physical systems by quality measures. Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng. 205, 105–117 (2016)

    Google Scholar 

  95. P. Merino, Laso, D. Brosset, J. Puentes, Analysis of quality measurements to categorize anomalies in sensor systems. IEEE Comput. Conf., 1330–1338 (2017). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8252077

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Authors and Affiliations

  1. IMT Atlantique, Lab-STICC, Technopole Brest Iroise – CS 83818, Brest, France

    John Puentes & Laurent Lecornu

  2. IMT Atlantique, Technopole Brest Iroise – CS 83818, Brest, France

    Basel Solaiman

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  1. John Puentes
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  2. Laurent Lecornu
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Correspondence to John Puentes .

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  1. IMT-Atlantique, Brest, France

    Éloi Bossé

  2. The State University of New York at Buffalo, Buffalo, NY, USA

    Galina L. Rogova

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Puentes, J., Lecornu, L., Solaiman, B. (2019). Data and Information Quality in Remote Sensing. In: Bossé, É., Rogova, G. (eds) Information Quality in Information Fusion and Decision Making. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-030-03643-0_17

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