In this paper, we extract spectral image features from a hyperspectral image database, and use big data technology to classify spectra hierarchically, to achieve the purpose of efficient database matching. In this paper, the LDMGI (local discriminant models and global integration) algorithm and big data branch definition algorithm are used to classify the features of the hyperspectral image and save the extracted feature data. Hierarchical color similarity is used to match the spectrum. By clustering colors, spectral information can be stored as chain nodes in the database, which can improve the efficiency of hyperspectral image database queries. The experimental results show that the hyperspectral images of color hyperspectral images are highly consistent and indistinguishable, and need to be processed by the machine learning algorithm. Different pretreatment methods have little influence on the identification accuracy of the LDMGI model, and the combined pretreatment has better identification accuracy. The average classification accuracy of the LDMGI model training set is 95.62%, the average classification accuracy of cross-validation is 94.36%, and the average classification accuracy of the test set is 89.62%. Therefore, using big data analysis technology to process spectral features in hyperspectral image databases can improve query efficiency and more accurate query results.
Big Data Analysis: Hyperspectral Image Processing for Agriculture Applications
