A Stepwise-then-Orthogonal Regression (STOR) with quality control for Optimizing the RFM of High-Resolution Satellite Imagery
There are two major problems in Rational Function Model (RFM) solution: (a) Data source error, including gross error, random error, and systematic error; and (b) Model error, including over-parameterization and over-correction issues caused by unnecessary RFM parameters and exaggeration of random error in constant term of error-in-variables (EIV) model, respectively. In order to solve two major problems simultaneously, we propose a new approach named stepwise-then-orthogonal regression (STOR) with quality control. First, RFM parameters are selected by stepwise regression with gross error detection. Second, the revised orthogonal distance regression is utilized to adjust random error and address the overcorrection problem. Third, systematic error is compensated by Fourier series. The performance of conventional strategies and the proposed STOR are evaluated by control and check grids generated from SPOT5 high-resolution imagery. Compared with the least squares regression, partial least squares regression, ridge regression, and stepwise regression, the proposed STOR shows a significant improvement in accuracy.
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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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