River width is an essential parameter for studying the river’s hydrological process and has been widely used to estimate the river discharge. The existing approaches to measuring river width are based on remotely sensed imagery such as MODIS, Landsat to identify the river, and then estimate the river width. In this work, an alternate approach for river width estimation is proposed using the under-explored modality Synthetic Aperture Radar (SAR) images. SAR, unlike the traditional electro-optical sensors, can penetrate the clouds and can be used to collect the data in all weather conditions and even during the night. In this work, the river identification process is manifested as a binary semantic segmentation task in SAR images. For this, two state of the art deep learning algorithms (U-Net, DeepLabV3+) are utilized for river identification and subsequent width measurement. The proposed approach (DeepRivWidth) is used to estimate the width of the river of the Mangalore–Udupi region of Coastal Karnataka (India). These rivers originate or pass through Western Ghats (UNESCO world heritage site), and the proposed river width measurement approach could provide critical input for ecologists besides assisting efficient water management of the region. The estimated width is compared with the manually measured width, and significant improvement in the accuracy was obtained compared to existing river width measurement approaches. Besides, the performance evaluation of semantic segmentation approaches for river identification on a publicly available dataset provides valuable insights into segmenting rivers in SAR images.

河流宽度是研究河流水文过程的重要参数，已被广泛用于估算河流流量。现有的测量河道宽度的方法是基于遥感影像，例如MODIS，Landsat来识别河道，然后估算河道宽度。在这项工作中，提出了一种使用未开发的模态合成孔径雷达（SAR）图像估算河流宽度的替代方法。SAR与传统的光电传感器不同，它可以穿透云层并可以在所有天气情况下甚至在夜间收集数据。在这项工作中，河流识别过程表现为SAR图像中的二进制语义分割任务。为此，我们采用了两种最先进的深度学习算法（U-Net，DeepLabV3 +）用于河流识别和随后的宽度测量。拟议的方法（DeepRivWidth）用于估算卡纳塔克邦（印度）沿海芒格洛尔-乌杜皮地区的河流宽度。这些河流起源或经过西高止山脉（联合国教科文组织世界遗产），拟议的河宽测量方法除了可以帮助该地区进行有效的水管理外，还可以为生态学家提供重要的投入。将估算的宽度与手动测量的宽度进行比较，与现有的河流宽度测量方法相比，在精度上有了显着的提高。此外，在公开可用的数据集上进行语义分割方法进行河流识别的性能评估可为在SAR图像中分割河流提供有价值的见解。