Abstract. In recent years, Unmanned Aerial System (UAS) has been applied to collect aerial images for mapping, disaster investigation, vegetation monitoring and etc. It is a higher mobility and lower risk platform for human operation, but the low payload and short operation time reduce the image collection efficiency. In this study, one nadir and four oblique consumer grade DSLR cameras composed multiple camera system is equipped on a large payload UAS, which is designed to collect large ground coverage images in an effective way. The field of view (FOV) is increased to 127 degree, which is thus suitable to collect disaster images in mountainous area. The synthetic acquired five images are registered and mosaicked as larger format virtual image for reducing the number of images, post processing time, and for easier stereo plotting. Instead of traditional image matching and applying bundle adjustment method to estimate transformation parameters, the IOPs and ROPs of multiple cameras are calibrated and derived the coefficients of modified projective transformation (MPT) model for image mosaicking. However, there are some uncertainty of indoor calibrated IOPs and ROPs since the different environment conditions as well as the vibration of UAS, which will cause misregistration effect of initial MPT results. Remaining residuals are analysed through tie points matching on overlapping area of initial MPT results, in which displacement and scale difference are introduced and corrected to modify the ROPs and IOPs for finer registration results. In this experiment, the internal accuracy of mosaic image is better than 0.5 pixels after correcting the systematic errors. Comparison between separate cameras and mosaic images through rigorous aerial triangulation are conducted, in which the RMSE of 5 control and 9 check points is less than 5 cm and 10 cm in planimetric and vertical directions, respectively, for all cases. It proves that the designed imaging system and the proposed scheme have potential to create large scale topographic map.
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