Marine oil spills have caused major threats to marine environment over the past few years.The early detection of the oil spill is of great significance for the prevention and control of marine disasters.At present,rem...Marine oil spills have caused major threats to marine environment over the past few years.The early detection of the oil spill is of great significance for the prevention and control of marine disasters.At present,remote sensing is one of the major approaches for monitoring the oil spill.Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy（H） and reflection entropy（A）.The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill.The findings show that the information extracted from（1-A）×（1-H） and（1-H）×A parameters is relatively evident effects.The results of extraction of the oil spill information based on H×A parameter are relatively not good.The combination of the two has something to do with H and A values.In general,when H〉0.7,A value is relatively small.Here,the extraction of the oil spill information using（1-A）×（1-H） and（1-H）×A parameters obtains evident effects.Whichever combined parameter is adopted,oil well data would cause certain false alarm to the extraction of the oil spill information.In particular the false alarm of the extracted oil spill information based on（1-A）×（1-H） is relatively high,while the false alarm based on（1-A）×H and（1-H）×A parameters is relatively small,but an image noise is relatively big.The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature.展开更多
In cognitive radio networks, Secondary Users (SUs) have opportunities to access the spectrum channel when primary user would not use it, which will enhance the resource utilization. In order to avoid interference to p...In cognitive radio networks, Secondary Users (SUs) have opportunities to access the spectrum channel when primary user would not use it, which will enhance the resource utilization. In order to avoid interference to primary users, it is very important and essential for SUs to sense the idle spectrum channels, but also it is very hard to detect all the channels in a short time due to the hardware restriction. This paper proposes a novel spectrum prediction scheme based on Support Vector Machines (SVM), to save the time and energy consumed by spectrum sensing via predicting the channels' state before detecting. Besides, spectrum utilization is further improved by using the cooperative mechanism, in which SUs could share spectrum channels' history state information and prediction results with neighbor nodes. The simulation results show that the algorithm has high prediction accuracy under the condition of small training sample case, and can obviously reduce the detecting energy, which also leads to the improvement of spectrum utilization.展开更多
基金The National Natural Science Foundation of China under contract No.41376183the Oceanography Public Welfare Scientific Research Project "Marine oil spill risk assessment and key technologies of emergency response integration and demonstration" under contract No.201205012
文摘Marine oil spills have caused major threats to marine environment over the past few years.The early detection of the oil spill is of great significance for the prevention and control of marine disasters.At present,remote sensing is one of the major approaches for monitoring the oil spill.Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy（H） and reflection entropy（A）.The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill.The findings show that the information extracted from（1-A）×（1-H） and（1-H）×A parameters is relatively evident effects.The results of extraction of the oil spill information based on H×A parameter are relatively not good.The combination of the two has something to do with H and A values.In general,when H〉0.7,A value is relatively small.Here,the extraction of the oil spill information using（1-A）×（1-H） and（1-H）×A parameters obtains evident effects.Whichever combined parameter is adopted,oil well data would cause certain false alarm to the extraction of the oil spill information.In particular the false alarm of the extracted oil spill information based on（1-A）×（1-H） is relatively high,while the false alarm based on（1-A）×H and（1-H）×A parameters is relatively small,but an image noise is relatively big.The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature.
基金Sponsored by the Youth Foundation of Beijing Univesity of Postsand Telecommunications(Grant No.2011RC0110)Director Foundation of Key Lab of Universal Wirelsess Communication of Ministry of Education(Grant No.ZRJJ-2010-3)Ministry of Industry and Information Technology of China(Grant No.2011ZX03001-007-03)
文摘In cognitive radio networks, Secondary Users (SUs) have opportunities to access the spectrum channel when primary user would not use it, which will enhance the resource utilization. In order to avoid interference to primary users, it is very important and essential for SUs to sense the idle spectrum channels, but also it is very hard to detect all the channels in a short time due to the hardware restriction. This paper proposes a novel spectrum prediction scheme based on Support Vector Machines (SVM), to save the time and energy consumed by spectrum sensing via predicting the channels' state before detecting. Besides, spectrum utilization is further improved by using the cooperative mechanism, in which SUs could share spectrum channels' history state information and prediction results with neighbor nodes. The simulation results show that the algorithm has high prediction accuracy under the condition of small training sample case, and can obviously reduce the detecting energy, which also leads to the improvement of spectrum utilization.
