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Research Article Open access CC BY 4.0

Land Cover Classification Schemes Using Remote Sensing Images: A Recent Survey

S. Natya, V. J. Rehna

Current Journal of Applied Science and Technology · pp. 1–11 · Published 2 Dec 2015

10.9734/BJAST/2016/22037

Abstract

Economic development and growth in population have prompted rapid changes to earth’s land cover over the last few decades, and there is every indication that the pace of these changes will accelerate in the future. Therefore, systematic evaluations of Earth’s land cover must be repeated at a frequency that allows monitoring of both long term trends as well as inter-annual variability, and at a level of spatial detail to allow study of land use patterns. Land cover analysis can be done most effectively through remote sensing images of various spatial, spectral and temporal resolutions to improve the selection of areas designed for agricultural, urban and/or industrial areas of a region. Astute efforts have been made in developing advanced classification algorithms and techniques for improving the accuracy of land cover classification. Recent image classification approaches for land cover pattern analysis have been brought together with their pros and cones by reviewing literatures, books, manuals and other related documents. Suitable classification algorithms may be chosen based on their performance, type of image and application area. Through this survey, various aspects regarding, preprocessing, classification and accuracy assessment, new and unique land cover products may be generated which could not be produced by earlier techniques.  

Land use planning land cover classification remote sensing image classification geographical information systems

References (28)

  1. 1 Remote Sensing and Image Interpretation [DOI]
  2. 2 A survey of image classification methods and techniques for improving classification performance [DOI]
  3. 3 An introduction to neural computing [DOI]
  4. 4 Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data [DOI]
  5. 5 What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS
  6. 6 Object-based classification of remote sensing data for change detection [DOI]
  7. 7 Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions [DOI]
  8. 8 Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands [DOI]
  9. 9 Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis [DOI]
  10. 10 Geostatistical classification for remote sensing: an introduction [DOI]
  11. 11 A Composite Semisupervised SVM for Classification of Hyperspectral Images [DOI]
  12. 12 A Novel Technique for Subpixel Image Classification Based on Support Vector Machine [DOI]
  13. 13 Object-oriented image analysis and scale-space: Theory and methods for modeling and evaluating multi-scale landscape structure
  14. 14 Sub-pixel land cover mapping for per-field classification [DOI]
  15. 15 A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications [DOI]
  16. 16 Review of remotely sensed imagery classification patterns based on object-oriented image analysis [DOI]
  17. 17 LAND USE CLASSIFICATION OF REMOTE SENSING IMAGE WITH GIS DATA BASED ON SPATIAL DATA MINING TECHNIQUES
  18. 18 Fuzzy contextual classification of multisource remote sensing images [DOI]
  19. 19 Land Cover Characterization and Mapping of South America for the Year 2010 Using Landsat 30 m Satellite Data [DOI]
  20. 20 Per‐field urban land use classification based on tax parcel boundaries [DOI]
  21. 21 Improving urban classification through fuzzy supervised classification and spectral mixture analysis [DOI]
  22. 22 A Study on Hyperspectral Remote Sensing Classifications
  23. 23 Crisp and fuzzy competitive learning networks for supervised classification of multispectral IRS scenes [DOI]
  24. 24 Designing a rule-based classifier using syntactical approach [DOI]
  25. 25 Multiple classifier combination for land cover classification of remote sensing image [DOI]
  26. 26 Advanced Classification of Lands at TM and Envisat Images of Mongolia [DOI]
  27. 27 Object-oriented land cover classification of HJ-1B CCD image through multiple classifier fusion [DOI]
  28. 28 Supervised Classification Performance of Multispectral Images [DOI]

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