Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China.
National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China.
Sensors (Basel). 2021 Nov 7;21(21):7397. doi: 10.3390/s21217397.
Rapid and accurate extraction of water bodies from high-spatial-resolution remote sensing images is of great value for water resource management, water quality monitoring and natural disaster emergency response. For traditional water body extraction methods, it is difficult to select image texture and features, the shadows of buildings and other ground objects are in the same spectrum as water bodies, the existing deep convolutional neural network is difficult to train, the consumption of computing resources is large, and the methods cannot meet real-time requirements. In this paper, a water body extraction method based on lightweight MobileNetV2 is proposed and applied to multisensor high-resolution remote sensing images, such as GF-2, WorldView-2 and UAV orthoimages. This method was validated in two typical complex geographical scenes: water bodies for farmland irrigation, which have a broken shape and long and narrow area and are surrounded by many buildings in towns and villages; and water bodies in mountainous areas, which have undulating topography, vegetation coverage and mountain shadows all over. The results were compared with those of the support vector machine, random forest and U-Net models and also verified by generalization tests and the influence of spatial resolution changes. First, the results show that the F1-score and Kappa coefficients of the MobileNetV2 model extracting water bodies from three different high-resolution images were 0.75 and 0.72 for GF-2, 0.86 and 0.85 for Worldview-2 and 0.98 and 0.98 for UAV, respectively, which are higher than those of traditional machine learning models and U-Net. Second, the training time, number of parameters and calculation amount of the MobileNetV2 model were much lower than those of the U-Net model, which greatly improves the water body extraction efficiency. Third, in other more complex surface areas, the MobileNetV2 model still maintained relatively high accuracy of water body extraction. Finally, we tested the effects of multisensor models and found that training with lower and higher spatial resolution images combined can be beneficial, but that using just lower resolution imagery is ineffective. This study provides a reference for the efficient automation of water body classification and extraction under complex geographical environment conditions and can be extended to water resource investigation, management and planning.
从高空间分辨率遥感图像中快速准确地提取水体对于水资源管理、水质监测和自然灾害应急响应具有重要价值。对于传统的水体提取方法，图像纹理和特征的选择较为困难，建筑物和其他地面物体的阴影与水体处于同一光谱范围内，现有的深度卷积神经网络难以训练，计算资源消耗大，方法无法满足实时要求。本文提出了一种基于轻量级 MobileNetV2 的水体提取方法，并将其应用于多传感器高分辨率遥感图像，如 GF-2、WorldView-2 和无人机正射影像。该方法在两个典型的复杂地理场景中进行了验证：用于农田灌溉的水体，形状破碎，呈狭长状，周围环绕着城镇和村庄的许多建筑物；以及山区水体，地形起伏，植被覆盖，遍布山体阴影。将结果与支持向量机、随机森林和 U-Net 模型进行了比较，并通过泛化测试和空间分辨率变化的影响进行了验证。首先，结果表明，MobileNetV2 模型从三种不同高分辨率图像中提取水体的 F1 得分和 Kappa 系数分别为 GF-2 的 0.75 和 0.72、Worldview-2 的 0.86 和 0.85 以及无人机的 0.98 和 0.98，均高于传统机器学习模型和 U-Net。其次，MobileNetV2 模型的训练时间、参数数量和计算量均远低于 U-Net 模型，大大提高了水体提取效率。第三，在其他更复杂的地表区域，MobileNetV2 模型仍能保持较高的水体提取精度。最后，我们测试了多传感器模型的效果，发现结合使用较低和较高空间分辨率图像进行训练是有益的，但仅使用较低分辨率图像则无效。本研究为在复杂地理环境条件下实现水体分类和提取的高效自动化提供了参考，可扩展到水资源调查、管理和规划。
