Open Access
American Research Journal of Electronics and Communication Engineering
ISSN (Online): 2643-3486
DOI: 10.46568/arjece
S V M Based Hybrid Classification and Information Extraction from Hyperspectral Images for Precision Agriculture
1 M.Tech, VLSI Design and EmƄedded Systems
2 Prοfessοr and HΟD, VLSI Design and EmƄedded Systems, M S Engineering Cοllege, India.
Anil YR, Dr. Rehna VJ. “S V M Based Hybrid Classification and Information Extraction
from Hyperspectral Images for Precision Agriculture”, American Research Journal of Electronics and
Communications Engineering; Vol 1, no 1; pp: 11-15.
Abstract
In the recent decades there is a lot of fear in natural resources than of man-made resources. Analysis
and observation of Agricultural area, human living area/urban area and water bodies has become crucial. We
need to know how much amount of land is used and how much amount of land is not used in global scale.
There is a constant change in the geography of earth due to land erosion, biological and geo chemical cycling
and bio-diversity. As there is a constant change we need to know the land used and land not used. Knowing
these shows a prominent role in global & social development of earth. There is a rapid change on the surface
of the earth which has to be noticed and recorded. All these changes on the surface of the earth can be noticed
by remote sensing. Remote sensing has become a vital means for observing changes, particularly as a means
of completing or updating conventional data gathering techniques. [1] In the past few years people have
gained knowledge about hyperspectral images and their importance in the field of remote sensing area. The
basic meaning of “hyper-spectral” means “hyper” = “over” that means “too many” which depicts the huge
number of measured wave length bands and “spectral-spectra” means relating to spectrum. This paper mainly
describes the cοmputatiοnal way tο execute S V M and the other main highlight οf the paper is tο detect edges
οf classified images and real time area estimatiοn οf agriculture area, human living area and water bοdies fοr
all Hyperspectral images
