The quantitative evaluation of application of hyperspectral data based on multi-parameters joint optimization

Abstract

In order to evaluate the mineral identification of the hyperspectral data and make a trade-off of the imaging system parameters, a quantitative evaluation approach based on the multi-parameters joint optimization is proposed for the hyperspectral remote sensing. In the proposed approach, the mineral identification is defined as the number of the minerals identified and the key imaging parameters employed include ground sample distance (GSD) and spectral resolution (SR). Certain limitations are found among parameters that are used for analyzing the imaging processes. The constraints include the industrial manufacturing level, application requirements and the quantitative relationship among the GSD, the SR and the signal-to-noise ratio (SNR). Regression analysis is used to investigate the quantitative relationship between the mineral identification and the key imaging system parameters. Then, an optimization model for the trade-off study is established by combining the regression equation with the constraints. The airborne hyperspectral image collected by Hymap is applied to evaluate the performance of the proposed approach. The experimental results reveal that the approach can achieve the evaluation of the mineral identification and the trade-off of key imaging system parameters. The error of the prediction is within one kind of mineral.