Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spa...Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spatial resolution,they are often interfered by clouds,haze and rain.As a result,it is very difficult to retrieve ground information from spectral remote sensing data under those conditions.Compared with spectral remote sensing tech-nique,passive microwave remote sensing technique has obvious superiority in most weather conditions.However,the main drawback of passive microwave remote sensing is the extreme low spatial resolution.Considering the wide ap-plication of the Advanced Microwave Scanning Radiometer-Earth Observing System(AMSR-E) data,an AMSR-E data unmixing method was proposed in this paper based on Bellerby's algorithm.By utilizing the surface type classifi-cation results with high spatial resolution,the proposed unmixing method can obtain the component brightness tem-perature and corresponding spatial position distribution,which effectively improve the spatial resolution of passive microwave remote sensing data.Through researching the AMSR-E unmixed data of Yongji County,Jilin Provinc,Northeast China after the worst flood and waterlogging disaster occurred on July 28,2010,the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.展开更多
Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with t...Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with the local minimizers of NMF. We present two novel initialization strategies that is based on CUR decomposition, which is physically meaningful. In the experimental test, NMF with the new initialization method is used to unmix the urban scene which was captured by airborne visible/infrared imaging spectrometer (AVIRIS) in 1997, numerical results show that the initialization methods work well.展开更多
基金Under the auspices of National Natural Science Foundation of China (No. 40971189)Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-340)China Postdoctoral Science Foundation (No. 20100471276)
文摘Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster.Although spectral remote sensing data have many advantages for ground information observation,such as real time and high spatial resolution,they are often interfered by clouds,haze and rain.As a result,it is very difficult to retrieve ground information from spectral remote sensing data under those conditions.Compared with spectral remote sensing tech-nique,passive microwave remote sensing technique has obvious superiority in most weather conditions.However,the main drawback of passive microwave remote sensing is the extreme low spatial resolution.Considering the wide ap-plication of the Advanced Microwave Scanning Radiometer-Earth Observing System(AMSR-E) data,an AMSR-E data unmixing method was proposed in this paper based on Bellerby's algorithm.By utilizing the surface type classifi-cation results with high spatial resolution,the proposed unmixing method can obtain the component brightness tem-perature and corresponding spatial position distribution,which effectively improve the spatial resolution of passive microwave remote sensing data.Through researching the AMSR-E unmixed data of Yongji County,Jilin Provinc,Northeast China after the worst flood and waterlogging disaster occurred on July 28,2010,the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.
文摘Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with the local minimizers of NMF. We present two novel initialization strategies that is based on CUR decomposition, which is physically meaningful. In the experimental test, NMF with the new initialization method is used to unmix the urban scene which was captured by airborne visible/infrared imaging spectrometer (AVIRIS) in 1997, numerical results show that the initialization methods work well.
