  In recent years, deep learning techniques (e.g., U-Net, DeepLab) have
achieved tremendous success in image segmentation. The performance of these
models heavily relies on high-quality ground truth segment labels.
Unfortunately, in many real-world problems, ground truth segment labels often
have geometric annotation errors due to manual annotation mistakes, GPS errors,
or visually interpreting background imagery at a coarse resolution. Such
location errors will significantly impact the training performance of existing
deep learning algorithms. Existing research on label errors either models
ground truth errors in label semantics (assuming label locations to be correct)
or models label location errors with simple square patch shifting. These
methods cannot fully incorporate the geometric properties of label location
errors. To fill the gap, this paper proposes a generic learning framework based
on the EM algorithm to update deep learning model parameters and infer hidden
true label locations simultaneously. Evaluations on a real-world hydrological
dataset in the streamline refinement application show that the proposed
framework outperforms baseline methods in classification accuracy (reducing the
number of false positives by 67% and reducing the number of false negatives by
55%).
