Exploring Semantic Relationships for Hierarchical Land Use Classification Based on Convolutional Neural Networks

authored by: C. Yang, F. Rottensteiner, C. Heipke
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.
Organisation(s): Institute of Photogrammetry and GeoInformation (IPI)
Type: Conference article
Journal: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume: 5
Pages: 599-607
No. of pages: 9
ISSN: 2194-9042
Publication date: 03.08.2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Earth and Planetary Sciences (miscellaneous), Environmental Science (miscellaneous), Instrumentation
Sustainable Development Goals: SDG 15 - Life on Land
Electronic version(s): https://doi.org/10.5194/isprs-annals-V-2-2020-599-2020 (Access: Open)