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.
Since aerial photography entered the stage of digital photogrammetry, digital aerial photography has replaced traditional optical film aerial photography to obtain digital images, becoming a major development trend in the field of photogrammetry. Compared with single-format CCD cameras, large-format aerial survey cameras have the characteristics of wide field of view and high resolution, which can effectively reduce the flight time of aerial photography and improve production efficiency. The AFC-900 large-format aerial survey camera designed by the Beijing Institute of Space Mechatronics has the largest format among cameras in the world with higher resolution and operating efficiency. This article mainly introduces the camera design, main technical parameters, photogrammetric processing technology and advantages of AFC-900, and looks forward to its application prospects.
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