The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 241–246, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-241-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 241–246, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-241-2020

  21 Aug 2020

21 Aug 2020

THE LAND COVER CLASSIFICATION USING A FEATURE PYRAMID NETWORKS ARCHITECTURE FROM SATELLITE IMAGERY

Q. Zhang, Y. Zhang, P. Yang, Y. Meng, S. Zhuo, and Z. Yang Q. Zhang et al.
  • The Third Institute of Photogrammetry and Remote Sensing, Ministry of Natural Resources, Chengdu, China

Keywords: Land cover classification, deep learning, High spatial resolution image, Feature pyramid networks, transfer learning

Abstract. Extracting land cover information from satellite imagery is of great importance for the task of automated monitoring in various remote sensing applications. Deep convolutional neural networks make this task more feasible, but they are limited by the small dataset of annotated images. In this paper, we present a fully convolutional networks architecture, FPN-VGG, that combines Feature Pyramid Networks and VGG. In order to accomplish the task of land cover classification, we create a land cover dataset of pixel-wise annotated images, and employ a transfer learning step and the variant dice loss function to promote the performance of FPN-VGG. The results indicate that FPN-VGG shows more competence for land cover classification comparing with other state-of-the-art fully convolutional networks. The transfer learning and dice loss function are beneficial to improve the performance of on the small and unbalanced dataset. Our best model on the dataset gets an overall accuracy of 82.9%, an average F1 score of 66.0% and an average IoU of 52.7%.