ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 599–607, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-599-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 599–607, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-599-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Aug 2020

03 Aug 2020

EXPLORING SEMANTIC RELATIONSHIPS FOR HIERARCHICAL LAND USE CLASSIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORKS

C. Yang, F. Rottensteiner, and C. Heipke C. Yang et al.
  • Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany

Keywords: hierarchical land use classification, CNN, geospatial database, aerial imagery, semantic relationships

Abstract. Land use (LU) is an important information source commonly stored in geospatial databases. Most current work on automatic LU classification for updating topographic databases considers only one category level (e.g. residential or agricultural) consisting of a small number of classes. However, LU databases frequently contain very detailed information, using a hierarchical object catalogue where the number of categories differs depending on the hierarchy level. This paper presents a method for the classification of LU on the basis of aerial images that differentiates a fine-grained class structure, exploiting the hierarchical relationship between categories at different levels of the class catalogue. Starting from a convolutional neural network (CNN) for classifying the categories of all levels, we propose a strategy to simultaneously learn the semantic dependencies between different category levels explicitly. The input to the CNN consists of aerial images and derived data as well as land cover information derived from semantic segmentation. Its output is the class scores at three different semantic levels, based on which predictions that are consistent with the class hierarchy are made. We evaluate our method using two test sites and show how the classification accuracy depends on the semantic category level. While at the coarsest level, an overall accuracy in the order of 90% can be achieved, at the finest level, this accuracy is reduced to around 65%. Our experiments also show which classes are particularly hard to differentiate.