The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into...The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.展开更多
文摘最小负载着色问题(minimum load coloring problem,MLCP)源于构建光通信网络的波分复用(wavelength division multiplexing,WDM)技术,是一个被证明的NP完全问题.由于NP完全问题有着随问题规模呈指数增长的解空间,因此启发式算法常被用来解决这类问题.在对国内外相关工作的深入分析基础上得知,现有的多类求解MLCP问题的启发式算法中局部搜索算法表现是最好的.研究针对当前求解MLCP问题的局部搜索算法在数据预处理和邻域空间搜索上的不足,提出了两点相应的优化策略:一是在数据的预处理阶段,提出一度顶点规则来约简数据的规模,进而减小MLCP问题的搜索空间;二是在算法的邻域空间搜索阶段,提出两阶段多重选择策略(twostage best from multiple selections,TSBMS)来帮助局部搜索算法在面对不同规模的邻域空间时可以高效地选择一个高质量的邻居解,它有效地提高了局部搜索算法在处理不同规模数据时的求解表现.将这个优化后的局部搜索算法命名为IRLTS.采用74个经典的测试用例来验证IRLTS算法的有效性.实验结果表明,无论最优解还是平均解,IRLTS算法在大多数测试用例上都明显优于当前表现最好的3个局部搜索算法.此外,还通过实验验证了所提策略的有效性以及分析了关键参数对算法的影响.
基金The National Natural Science Foundation of China under contract No.42001401the China Postdoctoral Science Foundation under contract No.2020M671431+1 种基金the Fundamental Research Funds for the Central Universities under contract No.0209-14380096the Guangxi Innovative Development Grand Grant under contract No.2018AA13005.
文摘The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.
