For farmland, water bodies, villages and other areas in the Julu, Hebei, the inversion of GF-1 surface reflectance was carried out. Based on time-series remote sensing images, the Sentinel-2 satellite surface reflectance product was used to compare and verify the surface reflectance inversion results of the Gaofen-1 satellite. From February 2020 to January 2021, a total of 10 sets of valid images were acquired, with 5*5 pixels as a sample unit, and the test results of a total of 210 samples showed that the average absolute error of the reflectance is within 0.03 with the farmland, villages and other ground objects, and the average absolute error of the reflectance for the water body is within 0.055. In terms of correlation coefficients with Sentinel 2 data, the average correlation coefficient between farmland and villages was 0.999, and the correlation coefficient for water bodies was low, 0.158. This algorithm performs well in the target areas of farmland and villages, which is not suitable for water targets.
The 16m spatial resolution of the Gaofen-1 satellite WFV camera (GF-1 WFV) has potential advantages in ground feature recognition. However, due to the lack of shortwave infrared channels, it is difficult to invert the ground surface reflectivity using the traditional dark pixel method. Taking GF-1 WFV apparent reflectance data and ground-based atmospheric data as input, an atmospheric correction algorithm based on the 6S radiation transmission model is constructed. In order to verify the accuracy of the algorithm, the inversion reflectance value of Dunhuang calibration site was compared and analyzed with the measured Gobi surface reflectance data. The results showed that the relative errors of the blue, green, red, and near-infrared bands were all within 6%; Comparing and analyzing the reflectance products of Sentinel-2 and GF-1 WFV, the results show that the relative errors of blue, green, red, and near-infrared bands are all within 4%.
GAOFEN-4 is a high-resolution optical remote sensing satellite on geosynchronous orbit, which has been found the potential to monitor the moving ships on the sea. Some classic moving ships target detection algorithms are studied. A new algorithm is proposed for the sequence images of GAOFEN-4 satellite, which can effectively suppress the interference of noise on moving ships target detection by performing the defogging of dark channel prior and a moving detection with visual background extractor. Some experiments are executed with real sequence images. It shows that the new algorithm is better than classic target detection algorithms for the sequence images.
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