Hyperspectral Image Classification using SVM with PCA
K Mounika, K U Aravind, M. Yamini, P. Navyasri, Satyabrata Dash, Vadhri Suryanarayana
Abstract
The recent advancement and popularities of remote sensing technology is increasing day by day. Due to this the uses of hyperspectral imaging is also gaining popularity. Feature classification of ground-truth from HSI is also a a popular research aspect and a great challenge which actually attracts more research attention. In our research, a brief description on image classification models using SVM, with PCA, has been described. The study has been carried upon one common hyperspectral datasets i.e., Indian Pines which comprise various landscape fields like dense vegetation, barren land, grasslands, etc. For noisy band reduction, PCA has been used
Topics & Concepts
Hyperspectral imagingSupport vector machineArtificial intelligenceComputer sciencePattern recognition (psychology)Ground truthVegetation (pathology)Contextual image classificationFeature (linguistics)Remote sensingPopularityImage (mathematics)GeographyPathologySocial psychologyPhilosophyMedicinePsychologyLinguisticsRemote-Sensing Image ClassificationRemote Sensing and Land UseRemote Sensing in Agriculture