RPC-Based Orthorectification for Satellite Images Using FPGA

Zhang, Rongting; Zhou, Guoqing; Zhang, Guangyun; Zhou, Xiang; Huang, Jing

doi:10.3390/s18082511

Cited by 9 publications

(5 citation statements)

References 46 publications

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“…The orthorectification of high-resolution satellite images is widely discussed in the literature [3,4], and several models have been developed for their orientation [5][6][7]. However, the availability of new images and models is always a reason for further research and development in search of increasingly optimized and efficient strategies [8,9]. Some commercial software have implemented rigorous, scientifically accredited models such as the well-known Toutin model [10], making such software [11,12] the reference for the most recently performed experiments.…”

Section: Methodsmentioning

confidence: 99%

ACYOTB Plugin: Tool for Accurate Orthorectification in Open-Source Environments

Baiocchi

Giannone

Monti

et al. 2019

IJGI

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High-resolution satellite images must undergo a geometric rectification process in order to be used for metrical purposes. This operation, called orthorectification, is necessary because of deformations mainly due to camera distortions and acquisition geometry. To correctly orthorectify an image, it is necessary to accurately reconstruct the photogrammetric-acquisition characteristics and the image position with respect to a reference system connected to the ground. This operation, called orientation, can be done using various mathematical models such as rigorous, rational polynomial function (RPF), and rational polynomial coefficient, or, according to some authors, rapid positioning coefficient (RPC) models. Orientation and orthorectification are usually performed within specific commercial software, but in QGIS, these complex operations can be performed using the open libraries of the Orfeo Tool Box (OTB). Unfortunately, instructions given by OTB developers lead to scarce results. In fact, the procedure proposed in OTB does not allow for the full exploitation of the potential of RPC models, on which OTB itself is based. As OTB is open-source software, a plugin was developed to overcome these limitations and exploit its full potential. In fact, OTB interfaces are unfortunately essential, and some necessary functions are missing. Therefore, a new QGIS plugin was developed in order to run the entire process in the most photogrammetrically and statistically correct way, and, at the same time, to simplify the relative procedures.

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Section: Methodsmentioning

confidence: 99%

ACYOTB Plugin: Tool for Accurate Orthorectification in Open-Source Environments

Baiocchi

Giannone

Monti

et al. 2019

IJGI

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“…The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM is usually refined via affine transformation based on some CPs [2]. This is defined as follows:…”

Section: Rpc Correctionmentioning

confidence: 99%

“…For the GF-1/GF-6 images corrected by the RFM, the geometric accuracy of the whole image and the local geometric distortions were analyzed: (1) For many GF-1 and GF-6 images, histograms of the RMSE values were statistically analyzed according to different countries, the results of which were used to evaluate the geometric accuracy of whole image. (2) The local geometric distortions can affect the accuracy of the refined RFM; therefore, eight GF-1 images and eight GF-6 images from different countries and with different geometric accuracies were selected to evaluate the local geometric distortions. For each image, the geometric error value of each CP was calculated; then, a plot of geometric errors and a histogram of geometric error values were obtained.…”

Section: Geometric Accuracy Analysismentioning

confidence: 99%

“…The rational function model (RFM) can achieve high accuracy in fitting various physical sensor models, and its interpolation calculation is stable and accurate. In addition, the rational function model has almost the same accuracy as the physical sensor model [2,3]. Space Imaging was first used to employ the RFM for IKONOS satellite images; the Open Geospatial Consortium (OGC) has adopted rational polynomial coefficients (RPCs) as an incidental image parameter standard since 1999 [4].…”

Section: Introductionmentioning

confidence: 99%

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Does the Rational Function Model’s Accuracy for GF1 and GF6 WFV Images Satisfy Practical Requirements?

Shan

Zhang

2023

Remote Sensing

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The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.

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“…The HPC model includes heterogeneous computer network, cloud, field programmable gate array, and cluster-based multi-central processing unit (CPU) and graphics processing unit (GPU) systems [15], [16], [17]. The high-efficiency processing algorithms are explored in remote sensing image with HPC, such as hyperspectral imaging and endmember extraction [18], [19], [20], [21]. They concluded that HPC technology exhibits a significant improvement in efficiency compared to traditional computing methods.…”

Section: Introductionmentioning

confidence: 99%

A Large-Batch Orthorectification Generation Method Based on Adaptive GPU Thread Parameters and Parallel Calculation

Zhou,

Hu,

Hong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Orthorectification reflects a large amount of real and objective information, such as the characteristics of images and the geometric accuracy of maps. Conducting a large batch of orthorectification is a process with high time cost owing to the pixelwise correction each image. A common approach is to use graphics processing unit (GPU) parallel computing to accelerate orthorectification processing. However, most of the existing GPU acceleration studies have adopted experimental testing methods to determine thread parameters, which are inapplicable to different GPUs and affect the GPU acceleration performance. We put forward an adaptive calculation method for GPU thread parameters based on the performance parameters of different GPUs and by simultaneously blocking the image automatically according to the GPU memory space. We used 112 ZY-3 images to test the adaptive GPU and compare it to a general GPU. The experimental results show the following: first, for a single ZY-3 image, the GPU acceleration by the adaptive calculation method presented in this article is 43.22% higher than that by the general GPU, and the correction time is 34.41 times faster than that of the central processing unit. The result of the automatic image blocking was the same as that of the artificial blocking. Second, the experimental performance on four different GPUs indicated that all GPUs exhibited a significant speed boost. Third, for large-batch images, the GPU acceleration by the adaptive GPU was 32.6% higher than that by the general GPU, which provides an adaptive optimization strategy for large-batch image orthorectification.

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RPC-Based Orthorectification for Satellite Images Using FPGA

Cited by 9 publications

References 46 publications

ACYOTB Plugin: Tool for Accurate Orthorectification in Open-Source Environments

ACYOTB Plugin: Tool for Accurate Orthorectification in Open-Source Environments

Does the Rational Function Model’s Accuracy for GF1 and GF6 WFV Images Satisfy Practical Requirements?

A Large-Batch Orthorectification Generation Method Based on Adaptive GPU Thread Parameters and Parallel Calculation

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