Publication

Abstract : Extracting region-of-interest (ROI) from very high-resolution remote sensing (RS) imagery is difficult due to irregular object boundaries, multi-scale spatial variations, and complex backgrounds-difficulties that are intensified by the extensive manual annotation required to generate high-quality training labels. To mitigate the annotation burden, a solution is to exploit the unlabeled images along with limited labeled images. In this paper, we propose a novel boundaryaware semi-supervised adversarial learning for region of interest extraction (BROIE) from RS images with limited number of labeled samples. To accomplish this, BROIE framework utilizes a single end-to-end UNet-based generator equipped with an EfficientNet encoder, UNet decoder, and a segmentation and edge refinement module (SAER) to perform both pseudo-label generation and ROI prediction. Dynamic tau-based confidence filtering selects reliable pseudo-labels from weakly annotated images, while adversarial training between the generator and a patch discriminator encourages reliable mask generation. Experiments on WHU building, EORSSD, and Vaihingen image benchmark datasets demonstrate improved ROI extraction in scarce-label scenarios, highlighting the robustness of the proposed model across diverse conditions.