Robust Joint Representation of Intrinsic Mean and Kernel Function of Lie Group for Remote Sensing Scene Classification
Chengjun Xu, Guobin Zhu, Jingqian Shu
Abstract
Remote sensing scene classification is used to label specific semantic categories for images. The current methods have achieved competitive performances, but they are only for Euclidean space samples. As a result, their representations are not robust for non-Euclidean space samples, which affects the classification accuracy. In this letter, we introduce the Lie group manifold into the traditional feature representation method and propose a novel intrinsic mean representation method within the Lie group. At the same time, the kernel function based on the sample of the Lie group is designed to further improve the robustness and accuracy of classification. In addition, our method achieves satisfactory performance on two public and challenging remote sensing data sets of UC Merced and NWPU-RESISC45.