ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Research on Automatic Mosaicking and Synthesis Processing Technology for Multi-Source Remote Sensing Images
Provisionally accepted
- 1Jiangsu Siji Technology Service Co., Ltd., Nanjing, China
- 2Nanjing University of Information Science and Technology, Nanjing, China
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Multi - source remote sensing image automatic mosaic and synthesis processing technology is the key to improving the utilization efficiency of remote sensing data. With the rapid develop-ment of diversified imaging platforms such as satellites, unmanned aerial vehicles (UAVs) and ground sensors, the heterogeneity of image data sources has become increasingly prominent, which makes the difficulty of mosaic and synthesis increase. This paper focuses on the auto-matic mosaic and synthesis processing technology of multi - source remote sensing images. Firstly, an adaptive block - weighted Wallis parallel color equalization algorithm fusing specific scene constraints is designed. It dynamically adjusts the block size of color equalization pro-cessing through the coefficient of variation, and optimizes the calculation of local color param-eters combined with bilinear interpolation, which avoids the color distortion of traditional glob-al algorithms and significantly improves the efficiency of radiometric correction. Moreover, an adaptive mosaic algorithm is introduced, and a space - constrained Markov Random Field (MRF) - Graph Cut seamline generation model is used to generate seamless synthetic images, which supports large - area coverage. This technology can be extended to environmental monitoring, disaster assessment and urban planning. It can automatically process massive multi - source da-ta and achieve high - precision synthesis.
Keywords: Color consistency optimization, Cross-source remote sensing image processing, Image automatic mosaicking and synthesis, MRF-Graph Cut seamlinegeneration, Wallis transform
Received: 24 Oct 2025; Accepted: 11 Dec 2025.
Copyright: © 2025 Cai, Ye, Sun, Wei, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jing Cai
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