主被动遥感融合辐射传输卷积神经网络水深反演方法

Bathymetric Inversion Method for Active-passive Remote Sensing Fused Radiative Transfer Information Convolutional Neural Networks

新一代的冰、云和陆地高程卫星2号ICESat-2的星载激光雷达系统能够提供参考测深数据,采用机器学习方法能够与被动光学遥感图像结合实现高效的水深估算。提出一种结合物理辐射传输信息和卷积神经网络的模型。物理辐射传输数据突显了浅水区域的光谱特征,而卷积神经网络结构则很好地考虑了水深测量点像素的周边信息。实验结果表明,自适应椭圆密度分割算法相较于标准的基于固定参数的密度聚类算法能够更好地追踪水深信息。在圣克罗伊岛使用包含物理辐射传输信息的结果的平均均方根差比未使用的结果低0.1 m,平均精度提高了约10%。物理辐射传输卷积神经网络模型反演的水深结果的精确度均超过95%,误差小于1.6 m,验证了它在水深反演中的性能,具备在大规模水深反演中应用的潜力。

The nearshore area is of paramount importance in the ecosystem.Accurate bathymetric maps which depict underwater topography play a key role in supporting activities such as coastal research,environmental management and marine spatial planning.The new generation of Ice,Cloud,and Land Elevation Satellite 2(ICESat-2)is equipped with an advanced Terrain Laser Altimeter System(ATLAS),which delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions.ATLAS data can be combined with passive optical remote sensing imagery to realize efficient bathymetry estimation using a machine learning approach.Therefore,this study proposes a Convolutional Neural Network(CNN)model for physical radiation transmission information,which combines optical radiation transmission information with CNN models.The physical radiation transfer data highlights the spectral characteristics of shallow water areas,while the CNN structure takes into account the surrounding information of water depth measurement point pixels well.An adaptive elliptic density segmentation algorithm approach is applied to generate training and test samples based on the spectral reflectance characteristics and radiative transfer properties of Sentinel-2,using the reference bathymetry points of ICESat-2 as priori training data.The training datasets are generated based on the spectral reflectance and radiative transfer features of Sentinel-2.Next,a convolutional neural network model is appied to establish a link with the reference bathymetric point of ICESat-2.Finally,a complete bathymetric map would be generated by feeding the spectral feature data of the entire Sentinel-2 image into the trained convolutional neural network model.The obtained results are analyzed to validate the methodology,and comprehensively explores the effects of ICESat-2 extracted bathymetry point accuracy,inversion model and atmospheric correction on the performance of satellite-based remote sensing bathymetry inversion results.The continuously updated digital elevation model field data on the island of St.Croix are used to verify the accuracy and robustness of the water depth maps generated by the physical radiation transfer CNN model.The experimental results show that the adaptive elliptical density segmentation algorithm can better track water depth information compared to the standard fixed parameter density clustering algorithm.The adaptive elliptical density segmentation algorithm well eliminates the noise points and reduces the impact of noisy bathymetric points on the subsequent bathymetric inversion.The CNN model containing physical radiation transmission information exhibits higher accuracy and the RMSE using the CNN model containing physical radiation transmission information is reduced by 10%compared to the model without physical radiation transmission information in St.Thomas.The accuracy of the inversion results of the physical radiation transfer CNN model for water depth exceeds 95%,with an error of less than 1.6 m in all t hree s tudy a reas.In addition,the RMSE of the error evaluation of the bathymetric results using the data including the diffuse attenuation factor is 1.59 m,with an accuracy of 97%,which are better than those of the bathymetric results trained without the a priori diffuse attenuation factor,and the inclusion of the diffuse attenuation factor of the optical nature in the inversion process is favorable for the shallow water depth inversion.T he I CESat-2 reference bathymetry data are used as the field data to validate the simulation estimation results,and the RMSE of the error evaluation is 1.78 m and the accuracy could reach 95%,which shows that the method is still valid and stable when using different data sources.The above results demonstrate the potential of the convolutional neural network modeling approach based on physical radiative transfer information in obtaining high-precision bathymetric information,which is expected to play an active role in the large-scale application of satellite-mounted LiDAR.