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

中文翻译：

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基于准共形映射核机器学习的Internet信息智能高光谱数据分类

智能互联网数据挖掘是AIoT（人工智能）的重要应用，因此有必要使用互联网数据（包括图像，视频和其他信息）构建大型培训样本。其中，高光谱数据库对于图像处理和机器学习也是必需的。互联网环境提供了丰富的高光谱数据资源，但是高光谱数据没有类别标签，对应用程序没有很高的价值。因此，通过基于机器学习的分类为这些高光谱数据标记类信息非常重要。在本文中，我们提出了一种基于准保形映射内核机器学习的智能高光谱数据分类算法，用于基于互联网的高光谱数据检索。贡献包括三点：提出了一种基于拟保形映射的多核学习网络框架，用于高光谱数据分类，马哈拉诺比斯距离核函数是具有比基于欧氏距离的核函数学习能力更高的判别能力的网络节点，并且具有度量类判别力的目标功能提出了寻找拟形映射投影的最优参数的能力。实验表明，该方案对高光谱图像分类和检索是有效的。提出了测量类判别能力的目标函数，以求准保形映射投影的最优参数。实验表明，该方案对高光谱图像分类和检索是有效的。提出了测量类判别能力的目标函数，以求准保形映射投影的最优参数。实验表明，该方案对高光谱图像分类和检索是有效的。