Himanshu Govil A.M.U.Aligarh

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Comparative evaluation of fuzzy based object-oriented image classification method with parametric and non-parametric classifiers. Himanshu Govil A.M.U.Aligarh. Objectives. Up to what level of classification can we perform on LISSIII/LISSIV data? - PowerPoint PPT Presentation

Transcript of Himanshu Govil A.M.U.Aligarh

Page 1: Himanshu  Govil A.M.U.Aligarh
Page 2: Himanshu  Govil A.M.U.Aligarh

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 be the 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?

Objectives

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Maximum Likelihood (ML)

(Parametric Classifier)

Object Based (OB)

(Fuzzy classifier)

Knowledge Based (KB)

(Non-parametric Classifier)

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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)

Page 5: Himanshu  Govil A.M.U.Aligarh

Sahaspur, Rampur and adjoining area (Dehradun dist.)

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Images

Preprocessing stages

Training Sites

Classification Methods

Prepare land use /land cover map

Accuracy Analysis

Comparison

Final results

LISS IVLISS III

Maximum LikelihoodKnowledge Based Object Based

Maximum LikelihoodKnowledge BasedObject Based

Ground Truth

Methodology FlowchartMethodology Flowchart

Separability analysis

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No. First Level Second Level Third Level

1. Built up land Residential

Industrial

2. Agriculture land Cropland

Fallow land

3. Forest Evergreen Dense/Open

4. Water bodies River Dry/Perennial

Water

NRSA LANDUSE/ LANDCOVER CLASSIFICATION SCHEME APPLIED ON STUDY AREA

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LISS III

LISS IV

FEATURE SPACE FOR LISS III AND LISS IV (MLC)

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SEPARABILITY ANALYSIS FOR LISS III AND LISS IV

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CLASSIFIED IMAGE OF LISS III AND IV (MLC)

LISS IV

LISS III

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SEGMENTATION PARAMETERS FOR OBJECT-ORIENTED METHOD

LISS IV

LISS III

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CLASS DESCRIPTION (OBJECT BASED)

Agriculture (LISSIII)

Agriculture (LISSIV)

Urban (LISSIII) Water (LISSIII)

Urban (LISSIV) Water (LISSIV)

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

LISS IV

LISS III

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Dry river/Industrial

Urban/Agriculture

FEATURE SPACE OF SPECTRALLY MIXED CLASSES

(LISS III OBJECT BASED CLASSIFICATION)

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Dry river/Industrial Residential/Dry river

Industrial/Urban

FEATURE SPACE OF SPECTRALLY MIXED CLASSES

(LISS IV OBJECT BASED CLASSIFICATION)

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LISS III, IV CLASSIFIED IMAGE (OBJECT BASED)

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RULES FOR EXPERT CLASSIFIER

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LISS IV CLASSIFIED IMAGE (EXPERT CLASSIFIER)

After Rule base classification

Before Rule base classification

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Table 6: Overall accuracies (OA) & Kappa (K) achieved through various classification methods.

Dataset Pixel based

Classification

approach(MLC)

Object based

Expert

classifier

Increase in

accuracy from

MLC to Object

Based

Increase in

accuracy from

MLC to Expert

classifier

LISS IV (OA) 71.59 89.26 80.94 17.67 9.35

LISS III (OA) 84.00 89.15 - 5.15 -

LISS IV (K) 62.57 86.04 74.88 23.47 12.31

LISS III (K) 80.33 86.66 - 6.33  

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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.

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