Automatic image water level measuring based on neural network
Image-based gauging stations can effectively densify river level monitoring network.However,accurate water level measurements cannot be obtained by using images due to the reasons that the river may have different appearances during the year.Therefore,a method combining deep learning and photogrammetry is introduced in this paper to realize automatic and reliable water level measuring.First-ly,SegNet and FCN in the convolutional neural network in deep learning were used to segment the water areas collected by Raspberry Pi camera,and the accuracy from the two convolutional neural networks is better than 98％.Secondly,the water boundary line generated by the segmentation is intersected with the digital elevation model obtained by UAV tilt photogrammetry,and the image information is converted into metric water level.The correlation between the reference water level from the standard water gauge and that measured by the image-based method can be up to 0.947,with a minimum average deviation of only 1.2 cm.The method introduced in this paper realizes the densification of river level monitoring network by using cameras,and it can provide accurate water level measurements.
convolutional neural networkwater area segmentationwater level gauging
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