基于粒子群优化神经网络的水深反演模型  

A Model for Water Depth Retrieval Based on Neural Network Optimized by Particle Swarm Optimization

作　　者：林位衡 黄文骞[1] 李广会 李加群 LIN Weiheng;HUANG Wenqian;LI Guanghui;LI Jiaqun(Department of Military Oceanography and Hydrography,Dalian Naval Academy,Dalian 116018,China;32023 Troops,Dalian 116018,China)

机构地区：[1]海军大连舰艇学院军事海洋与测绘系,辽宁大连116018 [2]32023部队,辽宁大连116018

出　　处：《海洋测绘》2020年第5期26-29,共4页Hydrographic Surveying and Charting

基　　金：国家自然科学基金(41871295)。

摘　　要：针对直接采用BP神经网络反演水深收敛速度慢,且易陷入局部最优的问题,提出了一种基于粒子群(PSO)优化BP神经网络的水深遥感新模型。该模型首先利用粒子群算法对BP神经网络的权重和阈值进行优化,然后将该优化值作为BP神经网络的初始值,最后再将PSO优化后的模型用于测试海区的反演精度评估。实验结果表明,该模型的网络收敛速度明显加快,水深反演的精度也得到提高。Aiming to the slowness of convergence speed and being easy to get into partial optimum when only using BP neural network for water depth retrieval,a new model based on PSO(particle swarm optimization)-optimized BP neural network is proposed in the paper.Firstly,the weight and threshold of BP neural network are optimized by PSO.Then,the optimal value is taken as the initial value of BP neural network.Finally,the PSO-BP model is used to test the accuracy of the test.The experimental results demonstrate that the convergence speed of network in proposed model is obviously accelerated and the accuracy of water depth retrieval has also been improved.

关 键 词：海洋遥感 水深反演 多光谱影像 粒子群优化 BP神经网络 权重阈值优化 

分 类 号：P237[天文地球—摄影测量与遥感]