标题：Bathymetry Model Based on Spectral and Spatial Multifeatures of Remote Sensing Image
作者：Wang, Yanhong; Zhou, Xinghua; Li, Cong; Chen, Yilan; Yang, Lei
作者机构：[Wang, Yanhong; Zhou, Xinghua; Chen, Yilan; Yang, Lei] Minist Nat Resources, Inst Oceanog 1, Dept Engn Ctr, Qingdao 266061, Shandong, Peoples R China. 更多
通讯作者：Wang, Yanhong;Wang, YH
通讯作者地址：[Wang, YH]Minist Nat Resources, Inst Oceanog 1, Dept Engn Ctr, Qingdao 266061, Shandong, Peoples R China.
来源：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
关键词：Remote sensing; Training; Sea measurements; Neural networks; Feature; extraction; Machine learning algorithms; Deep learning; Bathymetry;; multilayer perceptron (MLP); multiple features; remote sensing
摘要：Multispectral methods for remote sensing image have been widely applied to shallow water bathymetry by researchers. In nonideal conditions, even with the same spectral radiance, the points still have a very wide range of water depths. This means that spectral features alone are insufficient for water bathymetry. Hence, we need to extract other valuable features from a remote sensing image. This letter introduces a spatial feature for water bathymetry using remote sensing images. We propose a model that utilizes a multilayer perceptron (MLP) to integrate the spectral and spatial location features. Experimental results demonstrate that the proposed model yields a substantial performance improvement. The mean relative error is only 8.41, and the root mean square error is reduced by 34-68 when compared with three other models. Furthermore, the proposed model addresses well the problems caused by heterogeneous bottom types.