RPC-Based Orthorectification for Satellite Images Using FPGA.

RPC-Based Orthorectification for Satellite Images Using FPGA.
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使用 FPGA 对卫星图像进行基于 RPC 的正射校正

DOI:
10.3390/s18082511
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发表时间:
2018-08-01
期刊:
Sensors (Basel, Switzerland)
影响因子:
--
通讯作者:
Huang J
Huang J
中科院分区:
其他
文献类型:
--
作者:
Zhang R;Zhou G;Zhang G;Zhou X;Huang J

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传统的基于有理多项式系数(RPC)的正射纠正方法已不能满足对恐怖袭击和灾难救援的及时响应要求。为了加快正射校正处理速度,我们提出了一种星上正射校正方法,即,一种基于现场可编程门阵列(FPGA)的定点(FP)-RPC正射校正方法。提出的RPC算法首先修改使用定点算法。然后,使用FPGA芯片实现FP-RPC算法。该方法分为三个主要模块:阅读参数模块、坐标变换模块和插值模块。应用两个数据集来验证可实现的处理速度和精度。与在PC机上使用Matlab实现的RPC方法相比,所提出的方法和基于Matlab的RPC方法的吞吐量分别为675.67 Mpixels/s和61070.24 pixels/s。这意味着,该方法比基于Matlab的RPC方法处理相同的卫星图像快约11,000倍。此外,对于第一研究区域,行坐标(Δ I)、列坐标(Δ J)和距离Δ S的均方根误差(RMSE)分别为0.35像素、0.30像素和0.46像素;而对于第二研究区域,它们分别为0.27像素、0.36像素和0.44像素,其满足实际中的校正精度要求。
Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area; and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice.
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