An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Obser...An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.展开更多
With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites,the volume of image datasets is growing exponentially.The management of big Earth data is also becoming...With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites,the volume of image datasets is growing exponentially.The management of big Earth data is also becoming increasingly complex and difficult,with the result that it can be hard for users to access the imagery that they are interested in quickly,efficiently and intelligently.To address these challenges,this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks.This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remotesensing images and keep model simple.The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision.In addition,the coverage rate and mean precision show a dramatic improvement of more than 40%compared with existing methods based on normal way.And,the coverage ratio gets 86%for the top 10 return results.展开更多
基金supported by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(XDA05040200)the National High-tech Research and Development Program(2011AA12A104)
文摘An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.
基金This work was supported by the National Natural Science Funds of China[41501116].
文摘With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites,the volume of image datasets is growing exponentially.The management of big Earth data is also becoming increasingly complex and difficult,with the result that it can be hard for users to access the imagery that they are interested in quickly,efficiently and intelligently.To address these challenges,this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks.This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remotesensing images and keep model simple.The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision.In addition,the coverage rate and mean precision show a dramatic improvement of more than 40%compared with existing methods based on normal way.And,the coverage ratio gets 86%for the top 10 return results.
