Abstract
With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs). Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM) is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST) algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.
Highlights
ZY-3 is the first civilian high-resolution stereo mapping satellite in China [1], and provides a tangible improvement in the monitoring capabilities for many fields, such as agriculture, forestry, geology, and so on
We put forward a new inherent error compensation model based on statistical learning theory to improve the geometric positioning accuracy of ZY-3 satellite images
Datasets of Beijing and Songshan areas were separately processed to conduct the free block adjustment together with an affine transformation model to compensate the bias of the rational polynomial coefficients (RPCs)
Summary
ZY-3 is the first civilian high-resolution stereo mapping satellite in China [1], and provides a tangible improvement in the monitoring capabilities for many fields, such as agriculture, forestry, geology, and so on. Some effective methods of block adjustment are proposed to achieve the accuracy requirement without using GCPs [6] Nowadays, such methods can be divided into two classes: using some other known information such as the digital elevation model (DEM) [12], or taking advantage of the stereo image pairs [13]. In 2010, a DEM-aided block adjustment method was presented by Teo et al [7] which is significant for improving the geometric consistency between overlapping images, and this method has been improved in [14] for nadir viewing images that constrains the elevations of tie points to improve the relative accuracy. Considering that ZY-3 satellite remote sensing images can satisfy the requirements of 1:50,000 scale topographic mapping, Cao [19] undertook block adjustment of multi-temporal images of ZY-3, and the residual errors were reduced because of the redundant observation.
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