Abstract
This chapter overviews earth observation imagery big data and its general classification methods. We introduce different types of earth observation imagery big data and their societal applications. We also summarize some general classification algorithms. Open computational challenges are also identified in this area.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J.B. Campbell, R.H. Wynne, Introduction to Remote Sensing. (Guilford Press, 2011)
NASA. MODIS Moderate Resolution Imaging Spectroradiometer, https://modis.gsfc.nasa.gov/
United States Geological Survey, Landsat Missions, https://landsat.usgs.gov/
European Space Agency, The Copernicus Open Access Hub, https://scihub.copernicus.eu/
United States Department of Agriculture, National Agricultural Imagery Program, https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/
National Oceanic and Atmospheric Administration, National Geodetic Survey, https://www.ngs.noaa.gov/
Google Earth Engine Team, Google earth engine: A planetary-scale geo-spatial analysis platform, https://earthengine.google.com (2015)
M.C. Hansen, P.V. Potapov, R. Moore, M. Hancher, S. Turubanova, A. Tyukavina, D. Thau, S. Stehman, S. Goetz, T. Loveland et al., High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013)
J.-F. Pekel, A. Cottam, N. Gorelick, A.S. Belward, High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016)
T. Kutser, L. Metsamaa, N. Strömbeck, E. Vahtmäe, Monitoring cyanobacterial blooms by satellite remote sensing. Estuar. Coast. Shelf Sci. 67(1), 303–312 (2006)
E. Eftelioglu, Z. Jiang, X. Tang, S. Shekhar, The nexus of food, energy, and water resources: Visions and challenges in spatial computing. in Advances in Geocomputation. (Springer, Berlin, 2017), pp. 5–20
G. Ruß, A. Brenning, Data mining in precision agriculture: management of spatial information. in Computational Intelligence for Knowledge-Based Systems Design. (Springer, Berlin, 2010), pp. 350–359
C. Zhang, J.M. Kovacs, The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13(6), 693–712 (2012)
E. Eftelioglu, Z. Jiang, R. Ali, S. Shekhar, Spatial computing perspective on food energy and water nexus. J. Environ. Stud. Sci. 6(1), 62–76 (2016)
A. Karpatne, Z. Jiang, R.R. Vatsavai, S. Shekhar, V. Kumar, Monitoring land-cover changes: A machine-learning perspective. IEEE Geosci. Rem. Sens. Mag. 4(2), 8–21 (2016)
D. Lu, Q. Weng, A survey of image classification methods and techniques for improving classification performance. Int. J. Rem. Sens. 28(5), 823–870 (2007)
R.G. Congalton, A review of assessing the accuracy of classifications of remotely sensed data. Rem. Sen. Environ. 37(1), 35–46 (1991)
A. Strahler, The use of prior probabilities in maximum likelihood classificaiton of remote sensing data. Rem. Sens. Environ. 10, 135–163 (1980)
M.A. Friedl, C.E. Brodley, Decision tree classification of land cover from remotely sensed data. Rem. Sens. Environ. 61(3), 399–409 (1997)
M. Pal, Random forest classifier for remote sensing classification. Int. J. Rem. Sens. 26(1), 217–222 (2005)
F. Melgani, L. Bruzzone, Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Rem. Sens. 42(8), 1778–1790 (2004)
J.A. Benediktsson, P.H. Swain, O.K. Ersoy, Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geosci. Rem. Sens. 28, 540–552 (1990)
G. Hay, G. Castilla, Geographic object-based image analysis (geobia): A new name for a new discipline. in Object-Based Image Analysis. (Springer, Berlin, 2008), pp. 75–89
Y. Tarabalka, J.A. Benediktsson, J. Chanussot, Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans. Geosci. Rem. Sens. 47(8), 2973–2987 (2009)
M. Fauvel, Y. Tarabalka, J.A. Benediktsson, J. Chanussot, J.C. Tilton, Advances in spectral-spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)
J.A. Benediktsson, J.A. Palmason, J.R. Sveinsson, Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Rem. Sens. 43(3), 480–491 (2005)
L. Breiman, Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Y. Freund, R. Schapire, N. Abe, A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(771–780), 1612 (1999)
L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)
S.E. Yuksel, J.N. Wilson, P.D. Gader, Twenty years of mixture of experts. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1177–1193 (2012)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Jiang, Z., Shekhar, S. (2017). Overview of Earth Imagery Classification. In: Spatial Big Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-60195-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-60195-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60194-6
Online ISBN: 978-3-319-60195-3
eBook Packages: Computer ScienceComputer Science (R0)