Scribble-Supervised Target Extraction Method Based on Inner Structure-Constraint for Remote Sensing Images
This work addresses target extraction for remote sensing applications, offering a weakly supervised approach that reduces labeling costs, but it is incremental as it builds on existing scribble-based methods with specific constraints.
The paper tackles the problem of target extraction in remote sensing images using sparse scribble annotations, which lack structural details, by proposing a method that integrates inner structure-constraints to improve localization and boundary accuracy, achieving superior performance over five state-of-the-art algorithms.
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.