DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
||||||||
Issue |
Volume-9,Issue-11 ( Nov, 2022 ) | |||||||||
Paper Title |
Semi Supervised SVM Learning Technique for Land-Cover Mapping Using Spectral Information | |||||||||
Author Name |
Srijani Das, Ujjwal Kumar Kamila, Abhishek Badyopadhyay Debasish Chakraborty | |||||||||
Affilition |
1B.Tech Student (CSE), Asansol Engineering College, Asansol, West Bengal 2Assistant Professor, Asansol Engineering College, Asansol, West Bengal 3Assistant Professor, Asansol Engineering College, Asansol, West Bengal 4Professor, Asansol Engineering College, Asansol, West Bengal | |||||||||
Pages |
31-35 | |||||||||
Abstract |
Abstract - In this article, an approach using semisupervised support vector machines (S3VMs) is investigated for the problem of multispectral image classification of remote sensing images. S3VMs are developed using the concept of maximizing margin on both labeled and unlabeled samples. The effectiveness of the proposed technique is first exhibited on two labeled remote sensing (RS) data represented in terms of feature vectors and then mapping different land cover types in RS imagery. Investigation on these datasets shows that employing additional unlabeled points alongwith original ground truth samples increases the accuracy level. Comparison is made with the existing methods in terms of number of training examples, kappa value, accuracy and quantitative cluster validity indices. Keywords - Landcover mapping, Remote sensing satellite images, Semisupervised SVM, RBF Kernel function, Quantitative cluster quality indices. | |||||||||
| View Paper | ||||||||||
