Proceedings Article | 29 August 2016
KEYWORDS: Hyperspectral imaging, Imaging systems, Calibration, Spectroscopy, Remote sensing, Reflectivity, Feature extraction, Vegetation, Spatial resolution, Data conversion
Normalized difference vegetation index, also known as the normalized difference vegetation index, is widely used in the study of vegetation and the research of plant phenology by means of remote sensing image. It is the best indicator of plant growth condition and the spatial distribution of vegetation density, bearing a linear relation with density of the vegetation distribution. NDVI can reflect the background image of plant canopy, such as soil, damp ground, withered leaves, roughness, etc. and it is also related to the vegetation. It has a variety of advantages, such as a higher detection sensitivity of vegetation, a higher detection range of vegetation coverage, the ability of eliminating the terrain and the community structure of shadows and radiated interference, the weakening of the noise brought by the angle of the sun and the atmosphere, etc. However, NDVI is also susceptible to canopy background variations, which lead to NDVI values of the soil pixels in plants shadows and vegetation pixels close in high spatial resolution data, thus, the separability of the foregoing two kinds of pixels of NDVI data is not satisfactory enough. In order to improve the separability of NDVI extracted from soil pixels and vegetation pixels, this paper, on the basis of NDVI, Red-band Enhanced Normalized Difference Vegetation Index (RNDVI) is constructed by introducing a red band strengthening coefficient , realize the nonlinear tensile of the NDVI value, as to increase the separability of vegetation pixels and the shadow of the soil pixels .On this basis, RNDVI threshold method and RNDVI-SVM method are employed to extract vegetation pixels from the high spatial resolution data obtained by Field Imaging Spectrometer System (FISS). The experimental results show that the accuracy of vegetation pixels extraction using RNDVI can be higher than using NDVI.
