With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched,the global volume of remotely sensed imagery has been growing exponentiall...With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched,the global volume of remotely sensed imagery has been growing exponentially.Processing the variety of remotely sensed data has increasingly been complex and difficult.It is also hard to efficiently and intelligently retrieve what users need from a massive database of images.This paper introduces an improved support vector machine(SVM)model,which optimizes the model parameters and selects the feature subset based on the particle swarm optimization(PSO)method and genetic algorithm(GA)for remote sensing image retrieval.The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID,PSO,and GA in terms of consistency and stability.展开更多
In this paper, we show that to retrieve specified objects in massive remote sensing data set is very important in both practice and theory. An algorithm-based content retrieval in the massive data set is studied. To a...In this paper, we show that to retrieve specified objects in massive remote sensing data set is very important in both practice and theory. An algorithm-based content retrieval in the massive data set is studied. To avoid the loss of information, the algorithm based on the Support Vector Machine classification is proposed. Also, the experiment on the real data set is made.展开更多
基金The authors would like to thank the Youth Council Project for the promotion of innovationas well as the Chinese Academy of Sciences and the National Natural Science Foundation for Young Scientists of China,No.40701105.
文摘With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched,the global volume of remotely sensed imagery has been growing exponentially.Processing the variety of remotely sensed data has increasingly been complex and difficult.It is also hard to efficiently and intelligently retrieve what users need from a massive database of images.This paper introduces an improved support vector machine(SVM)model,which optimizes the model parameters and selects the feature subset based on the particle swarm optimization(PSO)method and genetic algorithm(GA)for remote sensing image retrieval.The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID,PSO,and GA in terms of consistency and stability.
文摘In this paper, we show that to retrieve specified objects in massive remote sensing data set is very important in both practice and theory. An algorithm-based content retrieval in the massive data set is studied. To avoid the loss of information, the algorithm based on the Support Vector Machine classification is proposed. Also, the experiment on the real data set is made.
