How to Evaluate and Remove the Weakened Bands in Hyperspectral Image Classification

Hyperspectral image classification is mainly based on the spectral information of land covers, but water vapor or Rayleigh scattering will weaken the surface reflectance under the effect of adjacent pixels, and thus lead to the reducing of the discriminative information for the subsequent classification tasks. Atmospheric correction for the weakened bands is one of the most traditional ways to deal with this issue, but as a complete atmospheric correction for both of them is difficult, maybe a systematic exclusion of the severely affected bands base on quantitative evaluation is a better choice. In this paper, an evaluation based weaken band exclusion method for the hyperspectral image classification is proposed, trying to remove the severely affected bands without further atmospheric correction. Specifically, an evaluation model to describe how the water vapor and Rayleigh scattering affect the surface reflectance is constructed, by using the statistical relationship between the radiative transfer model and the band weaken index of spectra among the adjacent pixels. And then, with a simulation experiment, it is shown that water vapor and Rayleigh scattering can really weaken the discriminative information of some specific bands, and the band weaken index can serve as an appropriate index to evaluate the weakening degree of those bands. Finally, on this basis, the total framework of evaluation based weaken band exclusion method is given. The effectiveness and the universality of our proposed method have been verified and compared on four representative tasks of the hyperspectral image classification.