Compare with the reflectance of land surface, ocean water is much less. And, the contribution of atmospheric molecules and aerosols plays an vital importance on the water-leaving reflectance inversion. In order to simplify the inversion process, we generate a look-up-table(LUT) that contains the observation geometry information, the aerosol optical depth(AOD), the exponent of Junge power law(V) and the other factors used to calculate the water-leaving reflectance. The AOD and V are determined using our previous iterative algorithm from dual near-infrared(NIR) and dual shortwave infrared( SWIR) channels, respectively. We compare the retrieved AOD and V with Aerosol Robotic Network(AERONET) measurement data to ensure the precision of aerosol information. The AERONET AOD at 550nm is 0.0876, and the inversed AOD from dual-NIR and dual-SWIR is 0.05-0.325 and 0.0373-0.98, respectively. For dual- NIR, there are 31.97% and 57.18% pixels with the AOD absolute relative error less than 10% and 20%, respectively. For dual-SWIR, there are 31.01% and 59.79%. Then, we use the retrieved aerosol information together with the observation geometry information to get the factors used to calculate the water-leaving reflectance through interpolation. Finally, we use the MODIS ocean color product to verify the water leaving reflectance calculated based on aerosol retrieved from NIR and SWIR, and the two calculated water-leaving reflectance are marked as ρNIR and ρSWIR. In the visible and near-infrared region, both of them are smaller than the product values. Despite the ρSWIR is larger than ρNIR, the overcorrection is much more serious in ρNIR.
Acoustic seafloor classification with multibeam backscatter measurements is an attractive approach for mapping seafloor properties over a large area. However, artifacts in the multibeam backscatter measurements prevent accurate characterization of the seafloor. In particular, the backscatter level is extremely strong and highly variable in the near-nadir region due to the specular echo phenomenon. Consequently, striped artifacts emerge in the backscatter image, which can degrade the classification accuracy. This study focuses on the striped artifacts in multibeam backscatter images. To this end, a calibration algorithm based on equal mean-variance fitting is developed. By fitting the local shape of the angular response curve, the striped artifacts are compressed and moved according to the relations between the mean and variance in the near-nadir and off-nadir region. The algorithm utilized the measured data of near-nadir region and retained the basic shape of the response curve. The experimental results verify the high performance of the proposed method.
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