Liu Guangchun, He Huan, Gao Yun, Zhang Guangbao, Cao Tianyu
School of Resources and Civil Engineering, Liaoning Institute of Science and Technology, Benxi, 117004, China.
Liaoning Province Seventh Geological Brigade Co., Ltd, Dandong, 118003, China.
Sci Rep. 2025 Jul 13;15(1):25321. doi: 10.1038/s41598-025-10979-y.
In today's rapidly developing remote sensing technology, accurately extracting geographic information from maps is crucial for many key areas such as urban planning, environmental monitoring, and traffic management. However, due to the complexity and variability of remote sensing images, effectively extracting road information from multi-scale geographic images remains a technical challenge. Therefore, the study innovatively proposes a fusion model for panchromatic and multi-spectral images and a fusion map geographic information extraction model from the perspectives of image fusion and road segmentation. Structural similarity and spatial correlation coefficients are crucial for assessing the effectiveness of model image fusion. The experimental results show that in the panchromatic and multispectral remote sensing image datasets, the structural similarity of the model reached 0.023, which was very close to the target value of 0, indicating that the model had excellent image fusion ability. Meanwhile, the spatial correlation coefficient value was also as high as 0.99, close to the target value of 1, further confirming the efficiency of the model in image fusion. Compared with other methods, the designed method had significant advantages in maintaining the continuity of road structure, which could more accurately identify and reproduce the continuity of roads and reduce errors in the extraction process. In summary, the research results are of great significance to improve the accuracy and efficiency of remote sensing image analysis, which not only can provide strong technical support for the application in the above related fields, but also can contribute to the further development and application of remote sensing technology in geographic information extraction.
在当今快速发展的遥感技术中，从地图中准确提取地理信息对于城市规划、环境监测和交通管理等许多关键领域至关重要。然而，由于遥感图像的复杂性和多变性，从多尺度地理图像中有效提取道路信息仍然是一项技术挑战。因此，该研究创新性地从图像融合和道路分割的角度提出了一种全色和多光谱图像融合模型以及一种融合地图地理信息提取模型。结构相似性和空间相关系数对于评估模型图像融合的有效性至关重要。实验结果表明，在全色和多光谱遥感图像数据集中，该模型的结构相似性达到0.023，非常接近目标值0，表明该模型具有出色的图像融合能力。同时，空间相关系数值也高达0.99，接近目标值1，进一步证实了该模型在图像融合方面的效率。与其他方法相比，所设计的方法在保持道路结构的连续性方面具有显著优势，能够更准确地识别和再现道路的连续性，并减少提取过程中的误差。综上所述，研究结果对于提高遥感图像分析的准确性和效率具有重要意义，不仅可为上述相关领域的应用提供有力的技术支持，也可为遥感技术在地理信息提取方面的进一步发展和应用做出贡献。
