DQKNet: Deep Quasiconformal Kernel Network Learning for Image Classification

Compared to traditional technology, image classification technology possesses a superior capability for quantitative analysis of the target and background, and holds significant applications in the domains of ground target reconnaissance, marine environment monitoring, and emergency response to sudden natural disasters, among others. Currently, the enhancement of spatial spectral resolution heightens the difficulty and reduces the efficiency of classification, posing a substantial challenge to the aforementioned applications. Hence, the classification algorithm is required to take both computing power and classification accuracy into account. Research indicates that the deep kernel mapping network can accommodate both computing power and classification accuracy. By employing the kernel mapping function as the network node function of deep learning, it effectively enhances the classification accuracy under the condition of limited computing power. Therefore, to address the issue of network structure optimization of deep mapping networks and the insufficient application of line feature learning and expression in existing network structures, considering the adaptive optimization of network structures, deep quasiconformal kernel network learning (DQKNet) is proposed for image classification. Firstly, the structural parameters and learning parameters of the deep kernel mapping network are optimized. This approach can adaptively adjust the network structure based on the distribution characteristics of the data and enhance the performance of image classification. Secondly, the computational network node optimization method of quasiconformal kernel learning is applied to this network, further elevating the performance of the deep kernel learning mapping network in image classification. The experimental results demonstrate that the improvement in the deep kernel mapping network from the perspectives of accounting children, mapping network nodes, and network structure can effectively enhance the feature extraction and classification performance of the data. On the five public datasets, the average AA, OA, and KC values of our algorithm are 91.99, 91.25, and 85.99, respectively, outperforming the currently most-advanced algorithms.