Open AccessPrinciple Component Analysis for Crop Discrimination using Hyperspectral Remote Sensing Data
Author(s) -
Pooja Vinod Janse,
Ratnadeep R. Deshmukh
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i9297.0710921
Subject(s) - spectroradiometer , hyperspectral imaging , principal component analysis , component (thermodynamics) , reflectivity , remote sensing , crop , vegetation (pathology) , focus (optics) , computer science , mathematics , pattern recognition (psychology) , environmental science , artificial intelligence , geography , optics , medicine , physics , pathology , thermodynamics , forestry
Crop discrimination is still very challenging issue for researcher because of spectral reflectance similarity captured in non-imaging data. The objective of this research work is to focus on crop discrimination challenge. We have used ASD FieldSpec4 Spectroradiometer for collection of leaf samples of four crops Wheat, Jowar, Bajara and Maize. We used vegetation indices and some spectral reflectance band for featuring our dataset. We applied Principle Component Analysis (PCA) for discrimination and it has been observed that when we use first and second principle component, it will give poor result but if third principle component is used then we get accurate and fine results.