Sun glint contamination is a significant problem for high-resolution remote sensing over aquatic environments. Sun glint is a particular issue for researchers using aerial imagery to assess shallow water benthic communities, as it may be wrongly classified as benthic substrates of interest. Although various methods are available to correct for sun glint using multispectral and hyperspectral imagery, no method has been developed to detect sun glint within high-resolution RGB imagery, such as that collected with drones. Here we developed an artificial neural network capable of automated detection of sun glint in high-resolution imagery. Training data were classified using an object-based image analysis workflow and a semantic image segmentation algorithm was developed based on a modified U-net architecture. The model correctly identified 99.58% of background in our test images, and 66.07% of sun glint in our test images. These accuracies were achieved despite a highly imbalanced dataset, with sun glint only accounting for 1.19% of pixels in the testing dataset. Overall, 99.18% of predictions in this model were correct. Given this, we contend that this algorithm is a simple solution for the instant detection of sun glint from high-resolution imagery. We offer this semantic image segmentation as an open source solution for the detection and classification of sun glint in high-resolution imagery.

太阳闪光污染是水生环境高分辨率遥感的一个重要问题。对于使用航拍图像评估浅水底栖群落的研究人员来说，阳光闪烁是一个特殊问题，因为它可能被错误地归类为感兴趣的底栖基质。尽管可以使用多种方法来使用多光谱和高光谱图像校正太阳闪烁，但尚未开发出检测高分辨率 RGB 图像（例如无人机收集的图像）中的太阳闪烁的方法。在这里，我们开发了一种人工神经网络，能够自动检测高分辨率图像中的太阳闪光。使用基于对象的图像分析工作流对训练数据进行分类，并基于改进的 U-net 架构开发了语义图像分割算法。该模型正确识别了 99。58% 的背景在我们的测试图像中，66.07% 的阳光在我们的测试图像中。尽管数据集高度不平衡，但仍实现了这些精度，阳光仅占测试数据集中像素的 1.19%。总体而言，该模型中 99.18% 的预测是正确的。鉴于此，我们认为该算法是从高分辨率图像中即时检测太阳闪光的简单解决方案。我们提供这种语义图像分割作为一种开源解决方案，用于检测和分类高分辨率图像中的阳光。我们认为该算法是从高分辨率图像中即时检测太阳闪光的简单解决方案。我们提供这种语义图像分割作为一种开源解决方案，用于检测和分类高分辨率图像中的阳光。我们认为该算法是从高分辨率图像中即时检测太阳闪光的简单解决方案。我们提供这种语义图像分割作为一种开源解决方案，用于检测和分类高分辨率图像中的阳光。