A Multi-Objective Gray Consistency Correction Method for Mosaicking Regional SAR Intensity Images with Brightness Anomalies

In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. Applying traditional color harmonization methods increases this issue, ca...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Remote sensing (Basel, Switzerland) Ročník 17; číslo 9; s. 1607
Hlavní autoři: Wang, Jiaying, Shen, Xin, Li, Deren, Li, Litao, Jiang, Yonghua, Pan, Jun, Lu, Zezhong, Yao, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.05.2025
Témata:
Algorithms
Anomalies
Artificial satellites in remote sensing
Brightness
brightness anomaly
Genetic algorithms
gray consistency
Gray scale
Methods
multi-objective optimization
Multiple objective analysis
Optimization
Optimization models
Pareto optimum
Pixels
Quadratic programming
quadratic programming (QP)
Radar imaging
Regions
Remote sensing
SAR intensity image
Signal to noise ratio
Sorting algorithms
Synthetic aperture radar
Iran
China
ISSN:2072-4292, 2072-4292
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. Applying traditional color harmonization methods increases this issue, causing a loss of brightness information. We propose a multi-objective gray consistency correction method designed explicitly for mosaicking regional SAR intensity images with brightness anomalies to address this. We constructed a two-objective optimization model to ensure regional image gray consistency and mitigate brightness information loss. The truncation values of brightness anomaly images were selected as decision variables, maximizing the overall gray consistency of overlapping image pairs and minimizing the number of pixels with grayscale values that were out of bounds as the objective functions. To synchronously solve the truncation values of brightness anomaly images and linear stretch parameters of all images, a hybrid framework that combines the non-dominated sorting genetic algorithm II (NSGA-II) with the quadratic programming (QP) algorithm was proposed. Two large-area experimental results show that the proposed method achieves a balanced optimization between gray consistency and brightness information loss for regional SAR intensity image mosaicking. Compared with the traditional method, our method reduces brightness information loss by 99.552–99.647% and 99.973–99.969%, respectively, while maintaining better peak signal-to-noise ratio performance.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2072-4292
2072-4292
DOI:10.3390/rs17091607