ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 75–82, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-75-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 75–82, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-75-2020

  03 Aug 2020

03 Aug 2020

SCENE CLASSIFICATION BASED ON THE INTRINSIC MEAN OF LIE GROUP

C. Xu1, G. Zhu1, and K. Yang2 C. Xu et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, China
  • 2School of Geodesy and Geomatics, Wuhan University, China

Keywords: Scene classification, Remote sensing, Intrinsic mean, Lie Group

Abstract. Remote Sensing scene classification aims to identify semantic objects with similar characteristics from high resolution images. Even though existing methods have achieved satisfactory performance, the features used for classification modeling are still limited to some kinds of vector representation within a Euclidean space. As a result, their models are not robust to reflect the essential scene characteristics, hardly to promote classification accuracy higher. In this study, we propose a novel scene classification method based on the intrinsic mean on a Lie Group manifold. By introducing Lie Group machine learning into scene classification, the new method uses the geodesic distance on the Lie Group manifold, instead of Euclidean distance, solving the problem that non-euclidean space samples could not be calculated by Euclidean distance directly. The experiments show that our method produces satisfactory performance on two public and challenging remote sensing scene datasets, UC Merced and SIRI-WHU, respectively.