Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to\nconstruct large training samples with the data from the internet, including images, videos, and other information. Among them,\na hyperspectral database is also necessary for image processing and machine learning. The internet environment provides\nabundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So,\nit is important to label the class information for these hyperspectral data through machine learning-based classification. In this\npaper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification\nalgorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mappingbased\nmultiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance\nkernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function\nlearning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of\nthe quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image\nclassification and retrieval.
Loading....