ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 275–282, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-275-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 275–282, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-275-2022
 
17 May 2022
17 May 2022

EFFECT OF LABEL NOISE IN SEMANTIC SEGMENTATION OF HIGH RESOLUTION AERIAL IMAGES AND HEIGHT DATA

A. Maiti, S. J. Oude Elberink, and G. Vosselman A. Maiti et al.
  • Department of Earth Observation Science, Faculty ITC, University of Twente, The Netherlands

Keywords: Deep Learning, Semantic Segmentation, Label Noise, Very High Resolution

Abstract. The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices.