The sweeping camera systems in the surveying and mapping industry are usually efficient in image acquisition, for the photography coverage of a single strip is relatively large. The triangulation angle of correspondence is overly tiny to adopt traditional block adjustment (BA). This study analysed the imaging principle of the Chinese APS7K comprehensive camera system and proposed an aerial triangulation method for the data this system acquired. The proposed method first determines the adjacent matrix from the POS data and trajectory information. The other part of the method is to overcome the weak relative geometry in a single strip by introducing the digital elevation model (DEM) data into the block adjustment scheme. The optimal solution of adjustment is obtained by iteratively solving the problem. We verified the optimisation's effectiveness by checking stitched orthophoto and check points from Google Earth. The results show that mosaic discrepancy is eliminated, the reprojection error is reduced to subpixel level, and positioning accuracy is better than 0.4 meter (ground sample distance is 0.2 meter) after adjustment with ground control points. Finally, the method’s shortcomings and prospects are summarized.

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Optical image dense matching is a crucial step in the process of generating digital surface models (DSMs). Many existing dense matching methods have adopted pixelwise matching strategy and have achieved precise matching results; however, the methods are time consuming and have limited efficiency in high surveying and mapping production. We introduce a bridge probability relaxation matching method for automatic DSM generation. The method adopts a coarse-to-fine hierarchical strategy and achieves high matching accuracy and fast processing speed simultaneously. This method builds a self-adaptive disparity surface model in a local area and constrains the disparity surface using the spatial relationship between feature points and adjacent pixels. Finally, the disparity is optimized by calculating the increment of the relaxation iteration probability. Experiments are based on different areas with different textures and terrain types. Compared with the DSMs derived from semi-global matching, our proposed approach achieves high levels of accuracy and efficiency in automatic DSM generation.

For decades, the design of remote sensors has to make trade-offs among many characteristics such as the field of view (FOV), spatial resolution, spectral resolution, radiometric resolution, and the number of bands. It’s inevitable to weaken some characteristics to enhance others. Moreover, these problems lead to using multi-sources of remote sensing data in practical projects where a single sensor can’t meet the relevant requirements. The Airborne Dual-mode High-resolution Hyperspectral Imager (ADHHI) provides a new solution to the above limitations by the technology of multi-camera stitching. In this way, many excellent but conflicting characteristics can be separated into different imaging sub-systems, and are combined together during data processing. Supported by related processing algorithms and software, ADHHI embeds many excellent characteristics into one system, such as high spatial resolution, high spectral resolution, and high radiometric resolution. Firstly, this paper picks some common imaging sensors to illustrate the problem of conflicting characteristics. Secondly, we introduce the camera structure and sensor parameters of ADHHI. Then, we sketch out the data processing workflow and elaborate on relevant principles and results of the whole geometric correction, such as homo-spectral stitching and hetero-spectral registration. Finally, this airborne hyperspectral imager’s advantages and application prospects are concluded.