Unsupervised Classification on Satellite image : Unsupervised Classification on Satellite image EXTERNAL GUIDE
Dr S.N OMKAR
Dept Of Aero Space Engineering
Indian Institute of Science
Bangalore INTERNAL GUIDE
Prof K.R Shyalaja
Dept of Computer Science
Dr Ambedkar Institute of Technology
Bangalore Slide 2: LISS-4 Modis image of kolar dist RAW SATELLITE IMAGE Aim of the Project Work : Aim of the Project Work Satellite Image Classification
Input: Satellite image of low Resolution of 1501x1501
Aim: Classification of Land cover and water bodies using unsupervised clustering based algorithms
Additional Aim: Reclassification of classified area
Output: labeled classified image
Performance analysis of classification Slide 4: Input
Satellite Image K-Means
Classification Mean Shift
Clustering ? Output
Classified satellite Image Re- Classification
Of Classified region Comparison & Performance analysis
Of different Technique Loop Holes of Existing System : Loop Holes of Existing System Only primitive methods are used in unsupervised classification of land cover & water bodies
Most of the work using mean shift is on Medical and simpler images.
Various work has not yet proved in a completely satisfactory way the competitiveness of satellite based methods compared with ground measures and aerial surveys Proposed System : The Proposed Methodology has been broadly divided
Satellite Image Classification using Clustered based advanced unsupervised clustering method/algorithms like K-means , Mean shift..etc
Mean shift clustering has not been used on satellite image for classification till now, according to literature survey.
Reclassification of classified subsets
Comparison & Performance/Accuracy evaluation of different techniques
Based on evaluation results planning to go for Optimization of classification algorithms using biological inspired technique like PSO (Particle swarm optimization) after first stage of project implementation Proposed System Slide 7: Original Satellite Image Classified Satellite Image Sample Input and output Reference : 1. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, Wiley, 2002
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