Random cross-observation intensity consistency method for large-scale SAR images mosaics: An example of Gaofen-3 SAR images covering China

Zhang, Guo; Cui, Hao; Wang, Taoyang; Li, Zhijiang; Jiang, Binbin; Li, Xin; Wang, Huabin; Zhu, Yu

doi:10.1016/j.isprsjprs.2019.08.013

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…Due to the limitations of a single image's coverage, it is frequently required to smoothly composite multiple images into a mosaic when analyzing and studying SAR data on a wide scale. Seasonal variations, radar wave signal attenuation, noise, and different gain mistakes, on the other hand, can produce large intensity variances across images [6]. The radiometric discrepancy lowers the visual quality of the mosaic images significantly.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques work well for processing images with similar content, as was previously indicated. The issue of error accumulation during radial propagation was recently resolved by Zhang et al [6] using a random cross-observation technique. They were also able to achieve intensity correction for a range of highresolution images using a regionally independent linear model, in accordance with a low-resolution reference image that was created.…”

Section: Introductionmentioning

confidence: 99%

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Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

Xu,

Hong,

Tong

et al. 2024

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

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When multiple synthetic aperture radar (SAR) images are stitched together, the intensity disconnects between them can have a significant impact on the mosaic's quality. Many approaches focus on decreasing the intensity differences between images while ignoring the issue of image quality improvement. This study provides an algorithm for color correction and naturalness restoration for multiple images with uneven luminance in order to generate high-quality mosaics.To increase the illuminance component's naturalness, the image is first divided into illuminance and reflectance components, and the illuminance component is subjected to adaptive luminance improvement and contrast enhancement. Using a color consistency optimization approach, the intensity disparities between illuminance components are subsequently minimized. The reflectance and enhanced illuminance components are then combined to produce an improved image. After that, the enhanced image is mosaicked using multi-band blending. Finally, the intensity differences between the enhanced images are further decreased using the block-based Wallis transform based on the mosaic.We assessed the proposed method on 402 Sentinel-1 images covering the majority of China's land area to verify its robustness. When compared to similar algorithms, our strategy reduces the color distance (CD) by about 36.72%, improves the average gradient (AG) by around 89.44%, and increases the patch-based contrast quality index (PCQI) by roughly 32.85%. The experimental outcomes reveal that our approach has considerable advantages in terms of color correction and image quality improvement, both visually and quantitatively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

Xu,

Hong,

Tong

et al. 2024

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

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“…Generally, the coverage of a single SAR image is relatively limited due to the limitations of the SAR sensor design [6], especially when dealing with high-resolution SAR images where the coverage is often smaller. Meeting the diverse needs of various research and applications is increasingly challenging.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Liu,

Wang,

Jiao

et al. 2024

Remote Sensing

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Currently, massive Synthetic Aperture Radar (SAR) images acquired from numerous SAR satellites have been widely utilized in various fields, and image mosaicking technology provides important support and assistance for these applications. The traditional mosaic method selects specific SAR images that can cover the region of interest (ROI) from redundant data to produce “One Map”. However, an SAR image suffers from severe geometric distortion, especially in mountainous areas, which inevitably reduces the utilization of mosaic image. Therefore, a multi-view data-based layover information compensation (MDLIC) method for SAR image mosaic is proposed, aiming to take full advantage of multi-view data to compensate for the missing information in the layover area of the SAR image. This is performed to improve the information content of the mosaic image and realize efficient thematic information extraction and analysis. First, the calculation of the object-space extent of all images and the division of object-space grid are completed on the basis of geometric and radiometric preprocessing. Then, according to the transformation relationship between the object-space and the image-space, the sampling rate image of each image corresponding to the object-space grid is generated, which determines the layover area and the layover degree in each image. Finally, the information compensation strategy is implemented in accordance with the sampling rate image to realize the compensation of the layover information. The feasibility and effectiveness of the MDLIC method are verified by using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets for experiments. The experimental results indicate that the MDLIC method can obtain mosaic images with richer information compared with the traditional method, while still providing satisfactory results.

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“…It gives users higher requirements in the range and frequency of satellite observation. Regarding the observation range, it has been extended from small target imaging, such as target reconnaissance or emergency imaging [1], to large-scale regional imaging in cities, provinces, or even a country [2]. Regarding the observation frequency, it has also developed from longterm single-frequency production to annual, quarterly, and even monthly production [3].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

Chen,

Shen,

Zhang

et al. 2023

Remote Sensing

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With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems.

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Random cross-observation intensity consistency method for large-scale SAR images mosaics: An example of Gaofen-3 SAR images covering China

Cited by 11 publications

References 40 publications

Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

Color Correction and Naturalness Restoration for Multiple Images With Uneven Luminance

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

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