Analysis and modeling of radiometric error caused by imaging blur in optical remote sensing systems |
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| Institution: | 1. Research Institute of Photonics, Dalian Polytechnic University, No. 1 Qinggongyuan, Ganjingzi District, Dalian 116034, China;2. Research Center for Space Optical Engineering, Harbin Institute of Technology, No. 2 Yikuang Street, Nangang District, Harbin 150080, China;1. Thermal Systems Group, ISRO Satellite Centre, Bangalore 560017, India;2. Department of Nanotechnology, Center for Post Graduate Studies, Visvesvaraya Institute of Advance Technology, Visvesvaraya Technological University, Bengaluru Region, Muddenahalli, Chikkaballapur District 562101, India;1. National Key Laboratory of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China;2. Northeast Forestry University, Harbin 150040, China;1. Mechanical Engineering College, Electronic Department, Heping Road, Shijiazhuang City 050003, China;2. 66393 Postdoctoral Science Research Workstation, Qiyi Road, Hebei Baoding City 071000, China;3. State Grid Hebei Electronic Power Company Maintenance Branch, XinHua Region, Zhongsheng Road No. 66, Shijiazhuang 050071, China;1. Université de Rennes 1, Institut de Physique, UMR 6251, CNRS/Université de Rennes 1, Campus de Beaulieu, Bât. 10B, 35042 Rennes Cedex, France;2. Arizona Materials Laboratory, 4715 East Fort Lowell Rd, Tucson, AZ 85712, USA |
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| Abstract: | Imaging blur changes the digital output values of imaging systems. It leads to radiometric errors when the system is used for measurement. In this paper, we focus on the radiometric error due to imaging blur in remote sensing imaging systems. First, in accordance with the radiometric response calibration of imaging systems, we provide a theoretical analysis on the evaluation standard of radiometric errors caused by imaging blur. Then, we build a radiometric error model for imaging blur based on the natural stochastic fractal characteristics of remote sensing images. Finally, we verify the model by simulations and physical defocus experiments. The simulation results show that the modeling estimation result approaches to the simulation computation. The maximum difference of relative MSE (Mean Squared Error) between simulation computation and modeling estimation can achieve 1.6%. The physical experimental results show that the maximum difference of relative MSE between experimental results and modeling estimation is only 1.29% under experimental conditions. Simulations and experiments demonstrate that the proposed model is correct, which can be used to estimate the radiometric error caused by imaging blur in remote sensing images. This research is of great importance for radiometric measurement system evaluation and application. |
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| Keywords: | Imaging blur Radiometric error Stochastic fractal characteristics |
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