Extract Simulation Based on High Spectrum Imaging Features Based on Semi-Supervised Algorithms
Hyperspectral image includes the information of space,spectrum and radiation of the object to be meas-ured.In addition,the image information has the characteristics of high dimension,weak spatial correlation and strong nonlinearity,leading to the confusion of spatial feature sequence and the difficulty of feature extraction.Therefore,this paper puts forward a method of extracting hyperspectral image features based on semi-supervised algorithm.At first,the semi-supervised algorithm was adopted to reduce the dimension of high-dimensional data in hyperspectral images,and then the image deblurring was completed based on the result.After that,hyperspectral image data were learned by the feature learning model,thus obtaining the deep features of the image.In the pixel space,the depth fea-tures were combined with spatial information.Finally,the extraction of hyperspectral image features was completed.Experimental results show that the proposed method has good clustering effect of feature points in feature space.Meanwhile,the recall and precision can be more than 95%,indicating that the application performance of the method is better.
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