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RPC-Based Orthorectification for Satellite Images Using FPGA
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-...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111837/ https://www.ncbi.nlm.nih.gov/pubmed/30071668 http://dx.doi.org/10.3390/s18082511 |
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author | Zhang, Rongting Zhou, Guoqing Zhang, Guangyun Zhou, Xiang Huang, Jingjin |
author_facet | Zhang, Rongting Zhou, Guoqing Zhang, Guangyun Zhou, Xiang Huang, Jingjin |
author_sort | Zhang, Rongting |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6111837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61118372018-08-30 RPC-Based Orthorectification for Satellite Images Using FPGA Zhang, Rongting Zhou, Guoqing Zhang, Guangyun Zhou, Xiang Huang, Jingjin Sensors (Basel) Article 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. MDPI 2018-08-01 /pmc/articles/PMC6111837/ /pubmed/30071668 http://dx.doi.org/10.3390/s18082511 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Rongting Zhou, Guoqing Zhang, Guangyun Zhou, Xiang Huang, Jingjin RPC-Based Orthorectification for Satellite Images Using FPGA |
title | RPC-Based Orthorectification for Satellite Images Using FPGA |
title_full | RPC-Based Orthorectification for Satellite Images Using FPGA |
title_fullStr | RPC-Based Orthorectification for Satellite Images Using FPGA |
title_full_unstemmed | RPC-Based Orthorectification for Satellite Images Using FPGA |
title_short | RPC-Based Orthorectification for Satellite Images Using FPGA |
title_sort | rpc-based orthorectification for satellite images using fpga |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111837/ https://www.ncbi.nlm.nih.gov/pubmed/30071668 http://dx.doi.org/10.3390/s18082511 |
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