ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 39–43, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-39-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 39–43, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-39-2020

  03 Aug 2020

03 Aug 2020

LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL

B. Liu, S. Du, and X. Zhang B. Liu et al.
  • Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China

Keywords: Land cover classification, CNN, VHR remote sensing images, DSM, CRF, Deep features

Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.