Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)classification.However,most available deep networks require ample and authentic samples to better train the models,which is expen...Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)classification.However,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical tasks.Existing few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of HSI.To solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot instances.Specifically,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of classes.Next,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling capability.Furthermore,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution alignment.On three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.展开更多
With the development of artificial intelligence,remote sensing scene interpretation task has attracted extensive attention,which mainly includes scene classification,target detection,hyperspectral classification,and m...With the development of artificial intelligence,remote sensing scene interpretation task has attracted extensive attention,which mainly includes scene classification,target detection,hyperspectral classification,and multi‐modal analysis.The remote sensing scene interpretation has effectively promoted the development of the Earth observation field.It was the intention for this Special Issue to serve as a platform for the publication of the most recent research concepts from remote sensing image.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62161160336 and Grant 42030111.
文摘Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)classification.However,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical tasks.Existing few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of HSI.To solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot instances.Specifically,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of classes.Next,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling capability.Furthermore,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution alignment.On three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.
文摘With the development of artificial intelligence,remote sensing scene interpretation task has attracted extensive attention,which mainly includes scene classification,target detection,hyperspectral classification,and multi‐modal analysis.The remote sensing scene interpretation has effectively promoted the development of the Earth observation field.It was the intention for this Special Issue to serve as a platform for the publication of the most recent research concepts from remote sensing image.
基金supported by the National Key Research and Development Program of China(2020YFA0607900,2020YFA0608003,and 2021YFC3101601)the National Natural Science Foundation of China(42125503 and 42075137)the National Key Scientific and Technological Infrastructure Project‘‘Earth System Science Numerical Simulator Facility”(Earth Lab)。
