ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 105-112, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-1/105/2016/
doi:10.5194/isprs-annals-III-1-105-2016
 
02 Jun 2016
Accuracy Validation of Large-scale Block Adjustment without Control of ZY3 Images over China
Bo Yang Computer School of Wuhan University, Collaborative Innovation Center of Geospatial Technology and State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Keywords: ZY3, Block adjustment , Without control, RPC, Virtual control point, Geometric accuracy Abstract. Mapping from optical satellite images without ground control is one of the goals of photogrammetry. Using 8802 three linear array stereo images (a total of 26406 images) of ZY3 over China, we propose a large-scale and non-control block adjustment method of optical satellite images based on the RPC model, in which a single image is regarded as an adjustment unit to be organized. To overcome the block distortion caused by unstable adjustment without ground control and the excessive accumulation of errors, we use virtual control points created by the initial RPC model of the images as the weighted observations and add them into the adjustment model to refine the adjustment. We use 8000 uniformly distributed high precision check points to evaluate the geometric accuracy of the DOM (Digital Ortho Model) and DSM (Digital Surface Model) production, for which the standard deviations of plane and elevation are 3.6 m and 4.2 m respectively. The geometric accuracy is consistent across the whole block and the mosaic accuracy of neighboring DOM is within a pixel, thus, the seamless mosaic could take place. This method achieves the goal of an accuracy of mapping without ground control better than 5 m for the whole China from ZY3 satellite images.
Conference paper (PDF, 1365 KB)


Citation: Yang, B.: Accuracy Validation of Large-scale Block Adjustment without Control of ZY3 Images over China, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 105-112, doi:10.5194/isprs-annals-III-1-105-2016, 2016.

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