title: Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery.
abstract: This work deals with multisensor data fusion to obtain landcover classification. The role of feature-level fusion using the Dempster-Shafer rule and that of data-level fusion in the MRF context is studied in this paper to obtain an optimally segmented image. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two examples of data fusion of optical images and a synthetic aperture radar image are presented, each set having been acquired on different dates. Classification accuracies of the technique proposed are compared with those of some recent techniques in literature for the same image data.
authors.affiliation: Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, India. anjan@maths.iitkgp.ernet.in
authors.forename: Anjan
authors.initials: A
authors.lastname: Sarkar
authors.forename: Anjan
authors.initials: A
authors.lastname: Banerjee
authors.forename: Nilanjan
authors.initials: N
authors.lastname: Banerjee
authors.forename: Siddhartha
authors.initials: S
authors.lastname: Brahma
authors.forename: B
authors.initials: B
authors.lastname: Kartikeyan
authors.forename: Manab
authors.initials: M
authors.lastname: Chakraborty
authors.forename: K L
authors.initials: KL
authors.lastname: Majumder
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014159:Transducers