With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on ...With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.展开更多
Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time...Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time, hyperspectral data represented in a large number of bands are usually highly correlated. In this paper, to overcome the small sample problem in hyperspectral image classification, correlation of spectral bands is fully utilized to generate multiple new sub-samples from each original sample. The number of labeled training samples is thus increased several times. Experiment results demonstrate that the proposed method has an obvious advantage when the number of labeled samples is small.展开更多
基金supported by the National Natural Science Foundation of China(61471191)the Aeronautical Science Foundation of China(20152052026)the Foundation of Graduate Innovation Center in NUAA(kfjj20170313)
文摘With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.
文摘Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time, hyperspectral data represented in a large number of bands are usually highly correlated. In this paper, to overcome the small sample problem in hyperspectral image classification, correlation of spectral bands is fully utilized to generate multiple new sub-samples from each original sample. The number of labeled training samples is thus increased several times. Experiment results demonstrate that the proposed method has an obvious advantage when the number of labeled samples is small.
