Wang Chunjie, Zhang Ti, Tao Liang, Lin Jiayuan
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China.
Engineering Research Center of South Upland Agriculture (Ministry of Education), College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China.
Sensors (Basel). 2025 Jul 7;25(13):4225. doi: 10.3390/s25134225.
In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image.
在利用无人机获取的重叠图像生成数字正射影像图（DOM）的过程中，由于缺乏显著的纹理特征，在水域经常会出现一些异常情况，如纹理拉伸或数据空洞。这些异常严重影响了最终DOM的视觉质量和数据完整性。在本研究中，我们试图通过用从单张无人机图像中裁剪出的完整水域替换整个异常水域，来消除示例DOM中的水面异常。首先使用多源种子填充算法和轮廓查找算法精确获取图像中的水面范围和边界。接下来，从正常水面和异常水面的边界选取控制点，并利用仿射平面坐标变换实现它们的空间对齐。最后，将正常水面覆盖到DOM上，以替换相应的异常水面。恢复后的水域在光谱一致性方面具有良好的视觉效果，并且与周围环境的纹理过渡也足够自然。根据RGB像素的标准差和均值，与原始DOM相比，恢复后的DOM质量有了很大提高。这些表明所提出的方法在恢复DOM中的异常水面方面具有良好的性能，特别是对于水面面积相对较小且可包含在单张无人机图像中的场景。
