2016 年 36 巻 2 号 p. 136-151
This paper presents an algorithm to select the effective bands for producing NDVI (Normalized Difference Vegetation Index) image using Hyperion hyperspectral data. The training data set of vegetation areas is prepared by extracting the digital number values from NIR (Near infrared) and RED bands. Exploratory factor analysis for the training data set indicates that the first and the second factor loadings correspond to NIR and RED bands, respectively, which can also be divided into two groups by those factor loadings.
Based on the results, “Band 98 and Band 30” can be selected corresponding to maximum values of the first and the second factor loadings, respectively. As another approach, “Band 95 and Band 29” are selected with “maximum values of communalities”. Furthermore, “Band51 and Band30” are also selected corresponding to the central wavelengths of RED and NIR wavelength range in OLI(Operational Land Imager), respectively.
Based on the aerial photograph, false color image and vegetation map, the comparative experiments for selected bands-based NDVI maps indicate that “communality” is superior to “factor loading” in selecting effective bands for NDVI mapping to avoid over- and under-estimating the values of NDVI, as well as to assure the robustness against the noise in Hyperion hyperspectral data.