A 64-bit orthorectification algorithm using fixed-point arithmetic

French, Joseph C.; Balster, Eric J.; Turri, William F.

doi:10.1117/12.2032411

Cited by 1 publication

(3 citation statements)

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“…As observed from Figure 24, the gray values of Figure 24a and Figure 24b are 0, which means the proposed method implemented using FPGA has the same accuracy with the implemented by MATLAB, and Visual Studio (C++) [55,56]. Figure 24c As observed form Table 3 through 6, the accuracy of the georeferenced image when implemented using FPGA can reach the requirements because its RMSEs are less than one pixel [54].…”

Section: Gray Value Comparisonmentioning

confidence: 76%

Section: Gray Value Comparisonmentioning

confidence: 80%

“…The gray differences between the Ref-Img and those georeferenced images implemented by MATLAB, Visual Studio (C++), and ENVI are obtained and shown in Figures 24 and 25. As observed from Figure 24, the gray values of Figure 24a,b are 0, which means the proposed method implemented using FPGA has the same accuracy with the implemented by MATLAB, and Visual Studio (C++) [55,56]. Figure 24c indicates that the difference image between Ref-Img and Img-ENVI has many pixels are not same.…”

Section: Gray Value Comparisonmentioning

confidence: 87%

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On-Board Georeferencing Using FPGA-Based Optimized Second-Order Polynomial Equation

et al. 2019

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For real-time monitoring of natural disasters, such as fire, volcano, flood, landslide, and coastal inundation, highly-accurate georeferenced remotely sensed imagery is needed. Georeferenced imagery can be fused with geographic spatial data sets to provide geographic coordinates and positing for regions of interest. This paper proposes an on-board georeferencing method for remotely sensed imagery, which contains five modules: input data, coordinate transformation, bilinear interpolation, and output data. The experimental results demonstrate multiple benefits of the proposed method: (1) the computation speed using the proposed algorithm is 8 times faster than that using PC computer; (2) the resources of the field programmable gate array (FPGA) can meet the requirements of design. In the coordinate transformation scheme, 250,656 LUTs, 499,268 registers, and 388 DSP48s are used. Furthermore, 27,218 LUTs, 45,823 registers, 456 RAM/FIFO, and 267 DSP48s are used in the bilinear interpolation module; (3) the values of root mean square errors (RMSEs) are less than one pixel, and the other statistics, such as maximum error, minimum error, and mean error are less than one pixel; (4) the gray values of the georeferenced image when implemented using FPGA have the same accuracy as those implemented using MATLAB and Visual studio (C++), and have a very close accuracy implemented using ENVI software; and (5) the on-chip power consumption is 0.659 W. Therefore, it can be concluded that the proposed georeferencing method implemented using FPGA with second-order polynomial model and bilinear interpolation algorithm can achieve real-time geographic referencing for remotely sensed imagery.

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Section: Gray Value Comparisonmentioning

confidence: 76%

Section: Gray Value Comparisonmentioning

confidence: 80%

Section: Gray Value Comparisonmentioning

confidence: 87%

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