# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 1 Introduction  
Segmentation of a river scene plays an important role in many fields such as the water hazard detection of unmanned ground vehicles [1], the navigation of unmanned ships [2], river analysis or flood monitoring by remote sensing [3; 4; 5; 6] and vision-based object monitoring on rivers. This study aims to recognize the river region in an image taken in outdoor scenes based on the water surface reflection mechanism, which is an important task in applications of intelligent video surveillance in river environments. Moreover, segmentation of the river scene is a representative case of complex image segmentation, which can serve as a reference for complex image segmentation. For water region segmentation, researchers have explored different kinds of methods that fall into three main categories--image processing-based methods, Machine Learning-based methods (including Deep Learning, Supervised Learning, Clustering, etc. ), and hardware-based methods. For image processing-based methods, [PERSON] et al. [7] combined the color and texture features to detect the water region according to the appearance characteristics of the river in the outdoor scene. [PERSON] [8] used the Region Growing method firstly to separate the obvious water region based on the brightness value. Then a designed texture feature is used to perform K-Means clustering on each 9 \(\times\) 9 small patch in the image, where the class with the smallest average value of texture is classified as the water region, where the detection of water region with shadow needs the aid of stereo vision. [PERSON] et al. [9] used the adaptive threshold Canny edge detection algorithm to detect the river boundary. The texture and structure of images are also widely used in related research in water scenes such as waterline detection [10] and maritime horizon line detection [11]. For MachineLearning-based methods, [PERSON] et al. [12] proposed a self-supervised algorithm to classify all the image patches in an image into water or not-water category by features of RGB, texture, and height. The results show high accuracy but this algorithm requires prior knowledge of horizon by hardware and is only applicable to images that conform to a specific structure. Moreover, with the development of Deep Learning, it has also been applied in water region segmentation. For example, [PERSON] et al. [13] proposed an online learning approach to recognize the water region for the USV in the unknown navigation environment using a convolutional neural network (CNN). [PERSON] et al. [14] innovatively used the Fully Connected Convolutional Network (FCN) to achieve water hazards detection on the road. Despite of high accuracy, the artificial neural network with complex structure needs to be pre-trained in many scenes before use and requires high computing power. For hardware-based methods, some studies have used various optical sensors such as laser radar [15], infrared camera [1], stereo camera [16; 17], and polarized camera [16; 18; 19] to easily realize water hazard detection based on the optical characteristics of waters [20]. These methods are still difficult to popularize in applications due to the cost and equipment complexity. The above methods have some defects. As [PERSON] [21] made the observation that the river has inhomogeneous appearance in outdoor scenes, the methods simply utilizing image features, whose underlying assumption is that river appearance is fairly uniform, remain problematic due to inhomogeneous appearance (such as shadow and changing illumination) and show bad performance. For the same reason, it is also inappropriate for Machine Learning-based methods using the global features of an image to train a classification model to segment itself. As for the hardware-based methods, they are beyond the scope of this paper. Since image processing technology has advantages of simplicity and interpretability, this study proposes a segmentation algorithm utilizing designed image features without machine learning.