IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA 52242, USA.
Department of Civil and Environmental Engineering, Dankook University, Gyeonggi-do 16890, Korea.
Sensors (Basel). 2021 Mar 31;21(7):2407. doi: 10.3390/s21072407.
Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red-green-blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.
河流遥感技术通过河流测深和悬浮泥沙、藻类水华、床质等的视觉检测等过程，比费力且昂贵的现场测量更有效地监测多种河流特性。红-绿-蓝（RGB）光学传感器已广泛应用于传统的河流遥感中。然而，由于它们的三个受限波段，它们依赖于视觉检查进行定性评估，并且仅限于进行定量和准确的监测。最近在河流领域高光谱成像方面的进展使得高光谱图像能够配备超过 150 个光谱波段。因此，可以使用低空无人驾驶飞行器（UAV）中的传感器以高空间分辨率定量表征各种河流特性。许多努力正在进行中，以充分利用河流研究中的高光谱带信息。尽管可以为卫星和有人驾驶飞机获取地理参考高光谱图像，但使用 UAV 进行的尝试很少。这主要是因为由于密集空间分辨率驱动的高度敏感和沉重的图像，在 UAV 上进行图像配准之上的线扫描图像的综合更加困难。因此，在本研究中，我们提出了一种实用技术，通过使用光流算法进行有效的图像配准，在基于 UAV 的河流高光谱成像中实现高空间精度。模板匹配算法是基于 RGB 的遥感中最常见的图像配准技术；然而，它们需要大量的计算，并且可能容易出错，因为需要根据用户做出关于各种参数的决策。此外，由于尚未广泛应用于高光谱图像，因此需要验证该技术的空间精度。与未应用图像配准技术的情况相比，所提出的技术将空间误差平均降低了 91.9%，与模板匹配相比，将空间误差平均降低了 78.7%。
