Upload
lana
View
47
Download
1
Embed Size (px)
DESCRIPTION
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
Citation preview
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
Maximum Likelihood (ML)
(Parametric Classifier)
Object Based (OB)
(Fuzzy classifier)
Knowledge Based (KB)
(Non-parametric Classifier)
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)
Sahaspur, Rampur and adjoining area (Dehradun dist.)
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
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
LISS III
LISS IV
FEATURE SPACE FOR LISS III AND LISS IV (MLC)
SEPARABILITY ANALYSIS FOR LISS III AND LISS IV
CLASSIFIED IMAGE OF LISS III AND IV (MLC)
LISS IV
LISS III
SEGMENTATION PARAMETERS FOR OBJECT-ORIENTED METHOD
LISS IV
LISS III
CLASS DESCRIPTION (OBJECT BASED)
Agriculture (LISSIII)
Agriculture (LISSIV)
Urban (LISSIII) Water (LISSIII)
Urban (LISSIV) Water (LISSIV)
FEATURE SPACE FOR LISS III AND LISS IV (OBJECT-ORIENTED)
LISS IV
LISS III
Dry river/Industrial
Urban/Agriculture
FEATURE SPACE OF SPECTRALLY MIXED CLASSES
(LISS III OBJECT BASED CLASSIFICATION)
Dry river/Industrial Residential/Dry river
Industrial/Urban
FEATURE SPACE OF SPECTRALLY MIXED CLASSES
(LISS IV OBJECT BASED CLASSIFICATION)
LISS III, IV CLASSIFIED IMAGE (OBJECT BASED)
RULES FOR EXPERT CLASSIFIER
LISS IV CLASSIFIED IMAGE (EXPERT CLASSIFIER)
After Rule base classification
Before Rule base classification
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
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.