Optimization of RFM Problem Using Linearly Programed <i>ℓ</i>₁-Regularization

Gholinejad, Saeid; Naeini, Amin Alizadeh; Amiri-Simkooei, A. R.

doi:10.1109/tgrs.2020.3045091

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…The GPU parallel computing algorithm for orthorectification is presented in Table I. First, the RFM model is constructed according to the RPC parameters and the original image coordinates according to (9) [35]…”

Section: B Adaptive Calculation Of the Gpu Thread Parameters And Imag...mentioning

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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Section: B Adaptive Calculation Of the Gpu Thread Parameters And Imag...mentioning

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

View full text Add to dashboard Cite

show abstract

“…W ITH the rapid development of the aerospace photography technology, an increasing number of highresolution optical satellite images have been captured. In recent years, 3D scene reconstruction with optical satellite images, which aims to utilize multiple 2D scene images to automatically recover the corresponding 3D scene structure, has attracted more and more attention in both the fields of remote sensing and computer vision [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction

Wang,

Shi,

Chen

et al. 2022

Preprint

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How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical RPC (rational polynomial camera) model which requires at least 39 GCPs (ground control points), however, it is not trivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only 4 GCPs. The proposed framework is composed of an affine dense reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the affine dense reconstruction stage, an affine dense reconstruction approach is explored for pursuing the 3D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3D affine structure is upgraded to a Euclidean one with 4 GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms three stateof-the-art methods in most cases. [1]

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An Adaptive Regularized Solution to Inverse Ill-Posed Models

Shen

Chen

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Optimization of RFM Problem Using Linearly Programed ℓ₁-Regularization

Cited by 6 publications

References 37 publications

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

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

Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction

An Adaptive Regularized Solution to Inverse Ill-Posed Models

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