Simultaneous empirical line calibration of multiple spectral images

The empirical line (EL) calibration is commonly used for atmospheric correction of remotely sensed spectral images and recovery of surface reflectance. Current methods for EL calibration are applied to single image using two (or more) reference targets. Considering cases with large number of (partially overlapped) images, only few scenes will include reference targets. Moreover, applying the estimated calibration coefficients of one image to other images can cause wrong results. Accordingly, the use of EL calibration is impractical for these cases. In this paper, we present a novel method for a simultaneous calibration of multiple images, which is called multiple image constrained empirical line (MIcEL). We present a generalized EL model that provide constrained results and is adaptable for large number of images. Given a set of images, we use available reference targets and tie points between overlapping images to calibrate all the images in the set simultaneously. Tie points are automatically extracted using scale-invariant feature transform (SIFT) method. Accuracy assessment of the MIcEL was carried out using real hyperspectral images and field measurements. The performance of MIcEL was compared to the quick atmospheric correction (QUAC) method. the results show that (comparable with respect to QUAC) the absolute accuracy of the MIcEL, with respect to filed measurements, is ~ ± 11%.