Journal cover Journal topic
AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
Journal topic
Articles | Volume 3
AGILE GIScience Ser., 3, 36, 2022
https://doi.org/10.5194/agile-giss-3-36-2022
AGILE GIScience Ser., 3, 36, 2022
https://doi.org/10.5194/agile-giss-3-36-2022
 
10 Jun 2022
10 Jun 2022

Assessing the Influences of Band Selection and Pretrained Weights on Semantic-Segmentation-Based Refugee Dwelling Extraction from Satellite Imagery

Yunya Gao1, Getachew Workineh Gella1, and Nianhua Liu2 Yunya Gao et al.
  • 1Christian Doppler Laboratory for geospatial and EO-based humanitarian technologies (GEOHUM), Department of Geoinformatics – Z_GIS, Paris Lodron University of Salzburg, Salzburg, Austria
  • 2Department of Geoinformatics – Z_GIS, Paris Lodron University of Salzburg, Salzburg, Austria

Keywords: remote sensing, refugee dwellings, semantic segmentation, band selection, pretrained weights

Abstract. This research assessed the influences of four band combinations and three types of pretrained weights on the performance of semantic segmentation in extracting refugee dwelling footprints of the Kule refugee camp in Ethiopia during a dry season and a wet season from very high spatial resolution imagery. We chose a classical network, U-Net with VGG16 as a backbone, for all segmentation experiments. The selected band combinations include 1) RGBN (Red, Green, Blue, and Near Infrared), 2) RGB, 3) RGN, and 4) RNB. The three types of pretrained weights are 1) randomly initialized weights, 2) pretrained weights from ImageNet, and 3) weights pretrained on data from the Bria refugee camp in the Central African Republic). The results turn out that three-band combinations outperform RGBN bands across all types of weights and seasons. Replacing the B or G band with the N band can improve the performance in extracting dwellings during the wet season but cannot bring improvement to the dry season in general. Pretrained weights from ImageNet achieve the best performance. Weights pretrained on data from the Bria refugee camp produced the lowest IoU and Recall values.

Publications Copernicus
Download
Citation