Level set method has been extensively used for image segmentation,which is a key technology of water extraction.However,one of the problems of the level-set method is how to find the appropriate initial surface parame...Level set method has been extensively used for image segmentation,which is a key technology of water extraction.However,one of the problems of the level-set method is how to find the appropriate initial surface parameters,which will affect the accuracy and speed of level set evolution.Recently,the semantic segmentation based on deep learning has opened the exciting research possibilities.In addition,the Convolutional Neural Network(CNN)has shown a strong feature representation capability.Therefore,in this paper,the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve,which only needs to describe the general outline of the water body,rather than the accurate edges.Compared with the traditional circular and rectangular zero-level set initialization method,this method can converge to the edge of the water body faster and more precisely;it will not fall into the local minimum value and be able to obtain accurate segmentation results.The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.展开更多
Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manu...Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 41771457 and 41601443]the Research Program of the Department of Natural Resources of Hubei Province of China[grant number ZRZY2020KJ03].
文摘Level set method has been extensively used for image segmentation,which is a key technology of water extraction.However,one of the problems of the level-set method is how to find the appropriate initial surface parameters,which will affect the accuracy and speed of level set evolution.Recently,the semantic segmentation based on deep learning has opened the exciting research possibilities.In addition,the Convolutional Neural Network(CNN)has shown a strong feature representation capability.Therefore,in this paper,the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve,which only needs to describe the general outline of the water body,rather than the accurate edges.Compared with the traditional circular and rectangular zero-level set initialization method,this method can converge to the edge of the water body faster and more precisely;it will not fall into the local minimum value and be able to obtain accurate segmentation results.The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.
基金supported by National Natural Science Foundation of China (Grant:41901296,62202147).
文摘Hyperparameters play a vital impact in the performance of most machine learning algorithms.It is a challenge for traditional methods to con-figure hyperparameters of the capsule network to obtain high-performance manually.Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a com-binatorial optimization problem.However,these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted.The inspiration for the hybrid rice optimization(HRO)algorithm is from the breeding technology of three-line hybrid rice in China,which has the advantages of easy implementation,less parameters and fast convergence.In the paper,genetic search is combined with the hybrid rice optimization algorithm(GHRO)and employed to obtain the optimal hyperparameter of the capsule network automatically,that is,a probability search technique and a hybridization strategy belong with the primary HRO.Thirteen benchmark functions are used to evaluate the performance of GHRO.Furthermore,the MNIST,Chest X-Ray(pneumonia),and Chest X-Ray(COVID-19&pneumonia)datasets are also utilized to evaluate the capsule network learnt by GHRO.The experimental results show that GHRO is an effective method for optimizing the hyperparameters of the capsule network,which is able to boost the performance of the capsule network on image classification.
