Virtual sensors: using data mining techniques to efficiently estimate remote sensing spectra

Abstract

Various instruments are used to create images of the earth and other objects the universe a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built a phased approach, with additional measurement capabilities added later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned the original design of the instrument. In still other cases, high-resolution spectral measurements may be too costly to perform on a large sample, and therefore, lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. This work addresses this problem using virtual sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to fill in unmeasured spectral channels spectrally poor (low spectral resolution) data. The models we use Are multilayer perceptrons, support vector machines (SVMs) with radial basis function kernels, and SVMs with mixture density Mercer kernels. We demonstrate this method by using models trained on the high spectral resolution Terra Moderate Resolution Imaging Spectrometer (MODIS) instrument to estimate what the equivalent of the MODIS 1.6-/spl mu/m channel would be for the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR/2) instrument. The scientific motivation for the simulation of the 1.6-/spl mu/m channel is to improve the ability of the AVHRR/2 sensor to detect clouds over snow and ice.