A Geometric and Radiometric Simultaneous Correction Model (GRSCM) Framework for High-Accuracy Remotely Sensed Image Preprocessing
The grey value g (x, y) of pixel on radiometric spectrum is regarded as a function of the geometric coordinates (x, y). Hence, there is a unity of opposite relationships between the geometric and radiometric information, such that, these two types of information cannot be separated. Therefore, this paper proposes a novel geometric and radiometric simultaneous correction model (GRSCM) framework inspired and developed from least squares matching (LSM). Based on the Gauss-Markov model, geometric and radiometric correction coefficients are integrated and solved by an iterative method with variable weights in the proposed model. Moreover, many state-of-theart models and methods can be integrated into the proposed general GRSCM framework. In the GRSCM of this paper, RANdom SAmple Consensus (RANSAC), stepwise regression and significance testing are integrated and used. The experimental results demonstrate that the accuracy of the GRSCM is significantly improved compared with that of geometric correction and radiometric correction separately.
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Document Type: Research Article
Publication date: September 1, 2017
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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