  Weakly supervised learning based on scribble annotations in target extraction
of remote sensing images has drawn much interest due to scribbles' flexibility
in denoting winding objects and low cost of manually labeling. However,
scribbles are too sparse to identify object structure and detailed information,
bringing great challenges in target localization and boundary description. To
alleviate these problems, in this paper, we construct two inner
structure-constraints, a deformation consistency loss and a trainable active
contour loss, together with a scribble-constraint to supervise the optimization
of the encoder-decoder network without introducing any auxiliary module or
extra operation based on prior cues. Comprehensive experiments demonstrate our
method's superiority over five state-of-the-art algorithms in this field.
Source code is available at https://github.com/yitongli123/ISC-TE.
