Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Himanshu Govil A.M.U.Aligarh PowerPoint Presentation
Download Presentation
Himanshu Govil A.M.U.Aligarh

Himanshu Govil A.M.U.Aligarh

128 Vues Download Presentation
Télécharger la présentation

Himanshu Govil A.M.U.Aligarh

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Comparative evaluation of fuzzy based object-oriented image classification method with parametric and non-parametric classifiers Himanshu Govil A.M.U.Aligarh

  2. Objectives • Up to what level of classification can we perform on LISSIII/LISSIV data? • Is any advantage of high spectral resolution of LISSIII over LISSIV. If yes than how can we use it for classification ? • Would object based classification method work on LISS III/LISSIV. If yes than what would bethe level of accuracy? • Would knowledge based classification give the appropriate result for low and medium resolution images? • Could we increase the accuracy of these classification methods?

  3. Maximum Likelihood (ML) (Parametric Classifier) • Object Based (OB) (Fuzzy classifier) • Knowledge Based (KB) (Non-parametric Classifier)

  4. DATA AND STUDY AREA • Satellite images of the area IRS-P6 LISS IV IRS-P6 LISS III • Toposheet of the area (1:50,000) • Field data (training sites, test sites, GPS locations)

  5. Sahaspur, Rampur and adjoining area (Dehradun dist.)

  6. LISS IV LISS III Methodology Flowchart Images Preprocessing stages Separability analysis Ground Truth Training Sites Maximum Likelihood Knowledge Based Object Based Classification Methods Accuracy Analysis Maximum Likelihood Knowledge Based Object Based Comparison Prepare land use /land cover map Final results

  7. NRSA LANDUSE/ LANDCOVER CLASSIFICATION SCHEME APPLIED ON STUDY AREA

  8. LISS III FEATURE SPACE FOR LISS III AND LISS IV (MLC) LISS IV

  9. SEPARABILITY ANALYSIS FOR LISS III AND LISS IV

  10. CLASSIFIED IMAGE OF LISS III AND IV (MLC) LISS III LISS IV

  11. SEGMENTATION PARAMETERS FOR OBJECT-ORIENTED METHOD LISS III LISS IV

  12. CLASS DESCRIPTION (OBJECT BASED) Water (LISSIII) Urban (LISSIII) Agriculture (LISSIII) Urban (LISSIV) Water (LISSIV) Agriculture (LISSIV)

  13. FEATURE SPACE FOR LISS III AND LISS IV (OBJECT-ORIENTED) LISS III LISS IV

  14. FEATURE SPACE OF SPECTRALLY MIXEDCLASSES (LISS III OBJECT BASED CLASSIFICATION) Dry river/Industrial Urban/Agriculture

  15. FEATURE SPACE OF SPECTRALLY MIXED CLASSES (LISS IV OBJECT BASED CLASSIFICATION) Dry river/Industrial Residential/Dry river Industrial/Urban

  16. LISS III, IV CLASSIFIED IMAGE (OBJECT BASED)

  17. RULES FOR EXPERT CLASSIFIER

  18. LISS IV CLASSIFIED IMAGE (EXPERT CLASSIFIER) Before Rule base classification After Rule base classification

  19. CONCLUSION • On LISS III and LISS IV images up to second and third level of classification is possible but consideration of accuracy is needed. • High spectral resolution of LISS III can provide some good results to separate classes as compare to LISS IV. • Object based classification can also be applicable on LISS III and LISS IV images. But in LISS III it needs more parameters as compare to LISS IV. • By the help of expert classifier the accuracy of maximum likelihood results can be improved by the help of some additional layers.

  20. Thanking you for your kind attention