Transformation from hyperspectral radiance data to data of other sensors based on spectral superresolution

Hyperspectral radiance spectra are the sensor's response, through its spectral response functions (SRFs), to the at-sensor radiance field. As the SRFs vary with sensors, hyperspectral radiance data need to be transformed for cross-calibration with another sensor or data simulation of a future sensor. In fact, the hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect. Current data transformation methods do not fully consider this spectral response effect in hyperspectral data, and it restricts the accuracy of the transformation and, hence, the accuracy of the cross-calibration and data simulation. The spectral response effect is modeled as a correlation-sampling process instead of convolution sampling to fit all the hyperspectral, multispectral, and panchromatic sensors. An iterative method is proposed to get a superresolution spectrum from the hyperspectral radiance spectrum according to the response model. Data of another sensor are simulated by responding to the superresolution spectrum. Validation experiments are performed using MODTRAN4-simulated spectrum, coincident Earth Observing-1 Hyperion and Advanced Land Imager (ALI) radiance data, and nearly coincident Hyperion and Landsat-7 Enhanced Thematic Mapper Plus data. For transformations from the MODTRAN4-simulated spectrum to the hyperspectral spectrum of different spectral resolutions, the proposed transformation method achieves the least root mean square relative error (RMSRE) compared with the linear interpolation, the cubic spline interpolation, and the deconvolution-sampling method. For transformation from the Hyperion data to the ALI multispectral and panchromatic data, the RMSRE is less than 9% and 4%, respectively.