Advertisement

Applied Examples

  • Surekha Borra
  • Rohit Thanki
  • Nilanjan Dey
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Satellite images provide much information about the Earth’s surface in a shorter period. The availability of various types of images (multitemporal, multispectral, multiresolution, and multisensory data) became a helpful tool in the evolution of remote sensing-based digital imaging. This chapter explores the applications of classification and clustering techniques when applied on multispectral and hyperspectral satellite images. The automatic analysis of satellite images aids in effective decision-making, thematic map creation, information extraction, disaster management, and field survey to name a few. The applications of satellite image classification in meteorology, oceanography, fishing, agriculture, biodiversity conservation, forestry, intelligence, crisis information, emergency mapping, disaster monitoring, and thermal applications are presented.

Keywords

Applications Crisis Disaster Land cover Thermal Satellite image analysis 

References

  1. 1.
    Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: A review. Journal of Plant Ecology, 1(1), 9–23.CrossRefGoogle Scholar
  2. 2.
    Vibhute, A., & Bodhe, S. K. (2012). Applications of image processing in agriculture: A survey. International Journal of Computer Applications52(2), 34–40.CrossRefGoogle Scholar
  3. 3.
    North, H., Pairman, D., Belliss, S. E., & Cuff, J. (2012, July). Classifying agricultural land uses with time series of satellite images. In 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5693–5696). IEEE.Google Scholar
  4. 4.
    Schmedtmann, J., & Campagnolo, M. L. (2015). Reliable crop identification with satellite imagery in the context of common agriculture policy subsidy control. Remote Sensing, 7(7), 9325–9346.CrossRefGoogle Scholar
  5. 5.
    Herbei, M., & Sala, F. (2016). Classification of land and crops based on satellite images Landsat 8: Case study SD Timisoara. Bulletin UASVM Series Agriculture, 73, 29–34.Google Scholar
  6. 6.
    Leslie, C. R., Serbina, L. O., & Miller, H. M. (2017). Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production (No. 2017-1034). US Geological Survey.Google Scholar
  7. 7.
    Crnojević, V., Lugonja, P., Brkljač, B. N., & Brunet, B. (2014). Classification of small agricultural fields using combined Landsat-8 and RapidEye imagery: Case study of Northern Serbia. Journal of Applied Remote Sensing, 8(1), 083512.CrossRefGoogle Scholar
  8. 8.
    Mohapatra, P., Chakravarty, S., & Dash, P. K. (2015). An improved cuckoo search based extreme learning machine for medical data classification. Swarm and Evolutionary Computation, 24, 25–49.CrossRefGoogle Scholar
  9. 9.
    Eurisy Report. (2011). Forest and biomass management using satellite information and services. Retrieved October, 2018, from https://www.eurisy.org/data_files/publications-documents/9/publications_document-9.pdf?t=1391446664.
  10. 10.
    Chatterjee, S., Datta, B., Sen, S., Dey, N., & Debnath, N. C. (2018, January). Rainfall prediction using hybrid neural network approach. In 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom) (pp. 67–72). IEEE.Google Scholar
  11. 11.
    Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., … Levizzani, V. (2014). Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research: Atmospheres, 119(9), 5128–5141.Google Scholar
  12. 12.
    Toté, C., Patricio, D., Boogaard, H., van der Wijngaart, R., Tarnavsky, E., & Funk, C. (2015). Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique. Remote Sensing, 7(2), 1758–1776.CrossRefGoogle Scholar
  13. 13.
    Maggioni, V., Meyers, P. C., & Robinson, M. D. (2016). A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era. Journal of Hydrometeorology, 17(4), 1101–1117.CrossRefGoogle Scholar
  14. 14.
    Prakash, S., Mitra, A. K., Pai, D. S., & AghaKouchak, A. (2016). From TRMM to GPM: How well can heavy rainfall be detected from space? Advances in Water Resources, 88, 1–7.CrossRefGoogle Scholar
  15. 15.
    Bajracharya, S. R., Shrestha, M. S., & Shrestha, A. B. (2017). Assessment of high-resolution satellite rainfall estimation products in a streamflow model for flood prediction in the Bagmati basin, Nepal. Journal of Flood Risk Management, 10(1), 5–16.CrossRefGoogle Scholar
  16. 16.
    Mukherjee, A., Dey, N., Kausar, N., Ashour, A. S., Taiar, R., & Hassanien, A. E. (2016). A disaster management specific mobility model for flying ad-hoc network. International Journal of Rough Sets and Data Analysis (IJRSDA), 3(3), 72–103.CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Voigt, S., Riedlinger, T., Reinartz, P., Künzer, C., Kiefl, R., Kemper, T., et al. (2005). Geo-information for disaster management. Germany: Springer.Google Scholar
  19. 19.
    Dymon, U. J. (1990). The role of emergency mapping in disaster response. FMHI Publications, Paper 45. Retrieved September, 2018, from http://scholarcommons.usf.edu/fmhi_pub/45.
  20. 20.
    Mittal, A. (2018). Disaster management using remote sensing technology. Retrieved September, 2018, from https://skymapglobal.com/disaster-management-remote-sensing/.
  21. 21.
    Voigt, S., Kemper, T., Riedlinger, T., Kiefl, R., Scholte, K., & Mehl, H. (2007). Satellite image analysis for disaster and crisis-management support. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1520–1528.CrossRefGoogle Scholar
  22. 22.
    Poser, K., & Dransch, D. (2010). Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica, 64(1), 89–98.Google Scholar
  23. 23.
    Tralli, D. M., Blom, R. G., Zlotnicki, V., Donnellan, A., & Evans, D. L. (2005). Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing, 59(4), 185–198.CrossRefGoogle Scholar
  24. 24.
    Hoque, M. A. A., Phinn, S., Roelfsema, C., & Childs, I. (2017). Tropical cyclone disaster management using remote sensing and spatial analysis: A review. International Journal of Disaster Risk Reduction, 22, 345–354.CrossRefGoogle Scholar
  25. 25.
    Er delj, M., Krol, M., & Notalzio, E. (2017). Wireless sensor networks and multi-unmanned aerial vehicle systems for natural disaster management. Computer Networks, 124, 72–86.CrossRefGoogle Scholar
  26. 26.
    Ciobotaru, A. M., Andronache, I., Dey, N., Petralli, M., Daneshvar, M. R. M., Wang, Q., … Pintilii, R. D. (2018). Temperature-humidity index described by fractal Higuchi dimension affects tourism activity in the urban environment of Focşani City (Romania). Theoretical and Applied Climatology, 1–11.Google Scholar
  27. 27.
    Ahmad, G. (2001). Mapping a dry shrub forest for biodiversity conservation planning. Unpublished M.Sc, International Institute for Geo-information Science and Earth Observation, Enschede.Google Scholar
  28. 28.
    Foody, G. M. (2008). GIS: Biodiversity applications. Progress in Physical Geography, 32(2), 223–235.CrossRefGoogle Scholar
  29. 29.
    Wang, K., Franklin, S. E., Guo, X., & Cattet, M. (2010). Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists. Sensors, 10(11), 9647–9667.CrossRefGoogle Scholar
  30. 30.
    Kuenzer, C., Ottinger, M., Wegmann, M., Guo, H., Wang, C., Zhang, J., … Wikelski, M. (2014). Earth observation satellite sensors for biodiversity monitoring: Potentials and bottlenecks. International Journal of Remote Sensing, 35(18), 6599–6647.CrossRefGoogle Scholar
  31. 31.
    González, M. P., Bonaccorso, E., & Papeş, M. (2015). Applications of geographic information systems and remote sensing techniques to conservation of amphibians in northwestern Ecuador. Global Ecology and Conservation, 3, 562–574.CrossRefGoogle Scholar
  32. 32.
    Khare, S., & Ghosh, S. K. (2016). Satellite remote sensing technologies for biodiversity monitoring and its conservation. International Journal of Advanced Earth Science and Engineering, 5(1), 375.CrossRefGoogle Scholar
  33. 33.
    Prasad, N., Semwal, M., & Roy, P. S. (2015). Remote sensing and GIS for biodiversity conservation. In Recent advances in lichenology (pp. 151–179). New Delhi: SpringerGoogle Scholar
  34. 34.
    Purnamasayangsukasih, P. R., Norizah, K., Ismail, A. A., & Shamsudin, I. (2016, June). A review of uses of satellite imagery in monitoring mangrove forests. In IOP Conference Series: Earth and Environmental Science (Vol. 37, No. 1, p. 012034). IOP Publishing.Google Scholar
  35. 35.
    Szantoi, Z., Brink, A., Buchanan, G., Bastin, L., Lupi, A., Simonetti, D., … Davy, J. (2016). A simple remote sensing-based information system for monitoring sites of conservation importance. In D. K. Upreti, P. K. Divakar, V. Shukla & R. Bajpai (Eds.), Remote Sensing in Ecology and Conservation, 2(1), 16–24.CrossRefGoogle Scholar
  36. 36.
    St-Louis, V., Pidgeon, A. M., Kuemmerle, T., Sonnenschein, R., Radeloff, V. C., Clayton, M. K., … Hostert, P. (2014). Modelling avian biodiversity using raw, unclassified satellite imagery. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 369(1643), 20130197.CrossRefGoogle Scholar
  37. 37.
    Lee, M., Kloog, I., Chudnovsky, A., Lyapustin, A., Wang, Y., Melly, S., … Schwartz, J. (2016). Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the South-eastern US 2003–2011. Journal of Exposure Science and Environmental Epidemiology, 26(4), 377.CrossRefGoogle Scholar
  38. 38.
    Jorge, S., Schuch, R. A., de Oliveira, N. R., da Cunha, C. E. P., Gomes, C. K., Oliveira, T. L., … Brod, C. S. (2017). Human and animal leptospirosis in Southern Brazil: A five-year retrospective study. Travel Medicine and Infectious Disease, 18, 46–52.CrossRefGoogle Scholar
  39. 39.
    Pacheco-González, R., Ellwood, E., Exeter, D., Stewart, A. W., Asher, I., & ISAAC Phase Three Study Group. (2016). Does urban extent from satellite images relate to symptoms of asthma, rhinoconjunctivitis and eczema in children? A cross-sectional study from ISAAC Phase Three. Journal of Asthma, 53(8), 854–861.CrossRefGoogle Scholar
  40. 40.
    Geng, G., Zhang, Q., Martin, R. V., van Donkelaar, A., Huo, H., Che, H., … He, K. (2015). Estimating long-term PM 2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model. Remote Sensing of Environment, 166, 262–270.CrossRefGoogle Scholar
  41. 41.
    Jerrett, M., Turner, M. C., Beckerman, B. S., Pope III, C. A., van Donkelaar, A., Martin, R. V., … Diver, W. R. (2016). Comparing the health effects of ambient particulate matter estimated using ground-based versus remote sensing exposure estimates. Environmental Health Perspectives, 125(4), 552–559.CrossRefGoogle Scholar
  42. 42.
    Almendros-Jiménez, J. M., Domene, L., & Piedra-Fernández, J. A. (2013). A framework for ocean satellite image classification based on ontologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), 1048–1063.CrossRefGoogle Scholar
  43. 43.
    Imagesat International (2017). Retrieved October, 2018, from https://www.maritime-executive.com/author/imagesat-international.
  44. 44.
    Corbane, C., Najman, L., Pecoul, E., Demagistri, L., & Petit, M. (2010). A complete processing chain for ship detection using optical satellite imagery. International Journal of Remote Sensing, 31(22), 5837–5854.CrossRefGoogle Scholar
  45. 45.
    Ludsin, S. A., Pangle, K. L., & Tyson, J. T. (2010). Using satellite imagery for fisheries management. Final completion report. Lake Erie Protection Fund, Toledo, Ohio.Google Scholar
  46. 46.
    Dassenakis, M., Paraskevopoulou, V., Cartalis, C., Adaktilou, N., & Katsiabani, K. (2011). Remote sensing in coastal water monitoring: Applications in the eastern Mediterranean Sea (IUPAC technical report). Pure and Applied Chemistry, 84(2), 335–375.CrossRefGoogle Scholar
  47. 47.
    Chandar Padmanaban, R., & Sudalaimuthu, K. (2012). Marine fishery information system and aquaculture site selection using remote sensing and GIS. International Journal of Advanced Remote Sensing and GIS, 1(1), 20.Google Scholar
  48. 48.
    Devi, G. K., Ganasri, B. P., & Dwarakish, G. S. (2015). Applications of remote sensing in satellite oceanography: A review. Aquatic Procedia, 4, 579–584.CrossRefGoogle Scholar
  49. 49.
    Diesing, M., Mitchell, P., & Stephens, D. (2016). Image-based seabed classification: What can we learn from terrestrial remote sensing? ICES Journal of Marine Science, 73(10), 2425–2441.CrossRefGoogle Scholar
  50. 50.
    de Souza, E. N., Boerder, K., Matwin, S., & Worm, B. (2016). Improving fishing pattern detection from satellite AIS using data mining and machine learning. PLoS ONE, 11(7), e0158248.CrossRefGoogle Scholar
  51. 51.
    Dauwalter, D. C., Fesenmyer, K. A., Bjork, R., Leasure, D. R., & Wenger, S. J. (2017). Satellite and airborne remote sensing applications for freshwater fisheries. Fisheries, 42(10), 526–537.CrossRefGoogle Scholar
  52. 52.
    Ferraz, A., Mallet, C., & Chehata, N. (2016). Large-scale road detection in forested mountainous areas using airborne topographic lidar data. ISPRS Journal of Photogrammetry and Remote Sensing, 112, 23–36.CrossRefGoogle Scholar
  53. 53.
    Li, M., Stein, A., Bijker, W., & Zhan, Q. (2016). Region-based urban road extraction from VHR satellite images using binary partition tree. International Journal of Applied Earth Observation and Geoinformation, 44, 217–225.CrossRefGoogle Scholar
  54. 54.
    Grinias, I., Panagiotakis, C., & Tziritas, G. (2016). MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 145–166.CrossRefGoogle Scholar
  55. 55.
    Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11–28.CrossRefGoogle Scholar
  56. 56.
    Chaudhuri, D., Kushwaha, N. K., Samal, A., & Agarwal, R. C. (2016). Automatic building detection from high-resolution satellite images based on morphology and internal gray variance. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 1767–1779.CrossRefGoogle Scholar
  57. 57.
    Samanta, S., Mukherjee, A., Ashour, A. S., Dey, N., Tavares, J. M. R., Abdessalem Karâa, W. B., … Hassanien, A. E. (2018). Log transform based optimal image enhancement using firefly algorithm for autonomous mini unmanned aerial vehicle: An application of aerial photography. International Journal of Image and Graphics, 18(4), 1850019.CrossRefGoogle Scholar
  58. 58.
    Chen, X., Xiang, S., Liu, C. L., & Pan, C. H. (2014). Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 11(10), 1797–1801.CrossRefGoogle Scholar
  59. 59.
    Cao, L., Wang, C., & Li, J. (2016). Vehicle detection from highway satellite images via transfer learning. Information Sciences, 366, 177–187.MathSciNetCrossRefGoogle Scholar
  60. 60.
    Xu, Y., Yu, G., Wang, Y., Wu, X., & Ma, Y. (2016). A hybrid vehicle detection method based on Viola-Jones and HOG + SVM from UAV images. Sensors, 16(8), 1325.CrossRefGoogle Scholar
  61. 61.
    Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281–330.CrossRefGoogle Scholar
  62. 62.
    Tang, T., Zhou, S., Deng, Z., Zou, H., & Lei, L. (2017). Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining. Sensors, 17(2), 336.CrossRefGoogle Scholar
  63. 63.
    Wu, H., Zhang, H., Zhang, J., & Xu, F. (2015, September). Fast aircraft detection in satellite images based on convolutional neural networks. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 4210–4214). IEEE.Google Scholar
  64. 64.
    Zhang, F., Du, B., Zhang, L., & Xu, M. (2016). Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5553–5563.CrossRefGoogle Scholar
  65. 65.
    Matese, A., Toscano, P., Di Gennaro, S. F., Genesio, L., Vaccari, F. P., Primicerio, J., … Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing, 7(3), 2971–2990.CrossRefGoogle Scholar
  66. 66.
    Wu, Q., Sun, H., Sun, X., Zhang, D., Fu, K., & Wang, H. (2015). Aircraft recognition in high-resolution optical satellite remote sensing images. IEEE Geoscience and Remote Sensing Letters, 12(1), 112–116.CrossRefGoogle Scholar
  67. 67.
    Zhao, A., Fu, K., Sun, H., Sun, X., Li, F., Zhang, D., et al. (2017). An effective method based on ACF for aircraft detection in remote sensing images. IEEE Geoscience and Remote Sensing Letters, 14(5), 744–748.CrossRefGoogle Scholar
  68. 68.
    Dey, N., Ashour, A. S., & Althoupety, A. S. (2017). Thermal imaging in medical science. In Recent Advances in Applied Thermal Imaging for Industrial Applications (pp. 87–117). IGI Global.Google Scholar
  69. 69.
    Prakash, A. (2000). Thermal remote sensing: Concepts, issues and applications. International Archives of Photogrammetry and Remote Sensing, 33(B1; PART 1), 239–243.Google Scholar
  70. 70.
    Menzel, W. P., & Satellite, N. O. A. A. (2005). Remote sensing applications with meteorological satellites. The Solar Spectrum, 3, 10.Google Scholar
  71. 71.
    Pappu, S., Akhilesh, K., Ravindranath, S., & Raj, U. (2010). Applications of satellite remote sensing for research and heritage management in Indian prehistory. Journal of Archaeological Science, 37(9), 2316–2331.CrossRefGoogle Scholar
  72. 72.
    Remondino, F. (2011). Heritage recording and 3D modeling with photogrammetry and 3D scanning. Remote Sensing, 3(6), 1104–1138.CrossRefGoogle Scholar
  73. 73.
    Agapiou, A., Lysandrou, V., Alexakis, D. D., Themistocleous, K., Cuca, B., Argyriou, A., … Hadjimitsis, D. G. (2015). Cultural heritage management and monitoring using remote sensing data and GIS: The case study of Paphos area, Cyprus. Computers, Environment and Urban Systems, 54, 230–239.CrossRefGoogle Scholar
  74. 74.
    Elfadaly, A., Lasaponara, R., Murgante, B., & Qelichi, M. M. (2017, July). Cultural heritage management using analysis of satellite images and advanced GIS techniques at East Luxor, Egypt and Kangavar, Iran (A comparison case study). In International Conference on Computational Science and Its Applications (pp. 152–168). Cham: Springer.CrossRefGoogle Scholar
  75. 75.
    Deroin, J. P., Kheir, R. B., & Abdallah, C. (2017). Geoarchaeological remote sensing survey for cultural heritage management. Case study from Byblos (Jbail, Lebanon). Journal of Cultural Heritage, 23, 37–43.CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Surekha Borra
    • 1
  • Rohit Thanki
    • 2
  • Nilanjan Dey
    • 3
  1. 1.Department of Electronics and Communication EngineeringK.S. Institute of TechnologyBengaluruIndia
  2. 2.Faculty of Technology and Engineering, Department of ECEC. U. Shah UniversityWadhwan cityIndia
  3. 3.Department of Information TechnologyTechno India College of TechnologyKolkataIndia

Personalised recommendations