Mapping water bodies from remotely sensed imagery is crucial for understanding hydrological and biogeochemical processes. The identification of water extent is mainly dependent on optical and synthetic aperture radar (SAR) images. However, the use of remote sensing for water body mapping is often undermined by the mixed pixel dilemma inherent to traditional hard classification approaches. At the same time, the presence of clouds in optical imagery and speckle noise in SAR imagery, coupled with the difficulty in differentiating between water-like surfaces and actual water bodies, significantly compromise the accuracy of water body identification. This paper proposes a DEEP feature collaborative convolutional neural network (CNN) for Water Super-Resolution Mapping based on Optical and SAR images (DeepOSWSRM), which collaboratively leverages Sentinel-1 and Sentinel-2 imagery to address the challenges of missing data and mixed pixels. The Sentinel-1 image provides complementary water distribution information for the cloudy areas of the Sentinel-2 image, while the Sentinel-2 image enhances the perception capabilities for small water bodies in the Sentinel-1 image. Using PlanetScope imagery as the true reference data, the effectiveness of the proposed method was assessed through two experimental scenarios: one utilizing synthetic coarse-resolution imagery degraded from Sentinel-1 and Sentinel-2 data and another using actual Sentinel-1 and Sentinel-2 data, encompassing both simulated and real cloud conditions. A comparative analysis was conducted against three state-of-the-art CNN-based water mapping methods and two CNN SRM methods. The findings demonstrate that the proposed DeepOSWSRM method successfully produces accurate, fine-resolution water body maps, with its performance mainly benefiting from the fusion of SAR and optical images.

中文翻译：

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通过融合 Sentinel-1 和 Sentinel-2 图像，使用特征协作 CNN 模型进行超分辨率水体测绘


从遥感图像绘制水体地图对于了解水文和生物地球化学过程至关重要。水域范围的识别主要依赖于光学和合成孔径雷达（SAR）图像。然而，利用遥感进行水体测绘往往会受到传统硬分类方法固有的混合像素困境的影响。同时，光学图像中云的存在和SAR图像中散斑噪声的存在，加上区分水体表面和实际水体的困难，严重影响了水体识别的准确性。本文提出了一种基于光学和 SAR 图像的水超分辨率测绘的深度特征协作卷积神经网络 (CNN) (DeepOSWSRM)，它协作利用 Sentinel-1 和 Sentinel-2 图像来解决丢失数据和混合像素的挑战。 Sentinel-1图像为Sentinel-2图像的多云区域提供了补充的水体分布信息，而Sentinel-2图像则增强了Sentinel-1图像中小水体的感知能力。使用 PlanetScope 图像作为真实参考数据，通过两种实验场景评估所提出方法的有效性：一种使用从 Sentinel-1 和 Sentinel-2 数据降级的合成粗分辨率图像，另一种使用实际的 Sentinel-1 和 Sentinel-2数据，包括模拟和真实的云条件。对三种最先进的基于 CNN 的水绘图方法和两种 CNN SRM 方法进行了比较分析。 研究结果表明，所提出的 DeepOSWSRM 方法成功地生成了准确、高分辨率的水体地图，其性能主要受益于 SAR 和光学图像的融合。