Hyperspectral remote sensing，which plays an important role in the field of earth observation and remote sensing

could be used to obtain more accurate and rich remote sensing information

thus covering various levels and full links of the various countries′ aerial

spaceflight and small range of ground observation.However

hyperspectral data sets are often very large and contain redundant information

which brings inconvenience to subsequent processing.In this study

Laplacian Eigen mapping was used to reduce the dimension and fulfil the feature extraction of hyperspectral data.Then a weighted naive Bayes classification algorithm was proposed

while the classic naive Bayes classifier was improved by the method of rewarding weight.The algorithm was verified by the open source data.Results indicated that the accuracy for the proposed method in identification of the object information reached to 92.7%

which was greatly improved compared with that for the traditional method.