Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 657-663, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-657-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 657-663, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-657-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

A NOVEL FRAMEWORK FOR REMOTE SENSING IMAGE SCENE CLASSIFICATION

S. Jiang1,2, H. Zhao1,2, W. Wu1,2, and Q. Tan1,2 S. Jiang et al.
  • 1Department of Civil Engineering, Tsinghua University, Beijing 10084, China
  • 23S Center, Tsinghua University, Beijing 10084, China

Keywords: Scene Classification, Deep Learning, Convolutional Neural Network, Fully-connected Layer, XGBoost

Abstract. High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.