Spectral data expansion and classification of ground objects based on semi empirical kernel driven model

Abstract

The spectral information data of ground objects refers to the relationship between spectral reflectance and wavelength. At present, the field imaging spectrometer is mainly used to obtain the image and spectral information of objects at the same time. However, the spectral reflectance of the same object in different directions is different, which seriously affects the accuracy of subsequent classification and target detection based on spectral data. In order to solve this problem, a method of spectral data expansion of ground objects based on semi empirical kernel driven model is proposed in this paper. A small amount of spectral data of ground objects under the condition of known directions are substituted into the model, and the spectral data under the condition of other arbitrary directions are inverted, which not only reduces the cost of sample collection, but also expands the spectral data of ground objects. Experiments prove the effectiveness of this spectral data expansion method and use the expanded spectral data as a priori sample for ground object classification. Compared with the classification method based on a small number of original spectral samples, the experiments show that this method can effectively improve the accuracy of ground object classification.