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
出版年:2020
卷:17
期:1
页码:37-41
DOI:10.1109/LGRS.2019.2915122
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.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077808403&doi=10.1109%2fLGRS.2019.2915122&partnerID=40&md5=4d94d336ff3580dceab53f49e5e9c3d9
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