Semantic segmentation is a crucial approach for remote sensing interpretation. High-precision semantic segmentation results are obtained at the cost of manually collecting massive pixelwise annotations. Remote sensing imagery contains complex and variable ground objects and obtaining abundant manual annotations is expensive and arduous. The semi-supervised learning (SSL) strategy can enhance the generalization capability of a model with a small number of labeled samples. In this study, a novel semi-supervised adversarial semantic segmentation network is developed for remote sensing information extraction. A multiscale input convolution module (MICM) is designed to extract sufficient local features, while a Transformer module (TM) is applied for long-range dependency modeling. These modules are integrated to construct a segmentation network with a double-branch encoder. Additionally, a double-branch discriminator network with different convolution kernel sizes is proposed. The segmentation network and discriminator network are jointly trained under the semi-supervised adversarial learning (SSAL) framework to improve its segmentation accuracy in cases with small amounts of labeled data. Taking building extraction as a case study, experiments on three datasets with different resolutions are conducted to validate the proposed network. Semi-supervised semantic segmentation models, in which DeepLabv2, the pyramid scene parsing network (PSPNet), UNet and TransUNet are taken as backbone networks, are utilized for performance comparisons. The results suggest that the approach effectively improves the accuracy of semantic segmentation. The F1 and mean intersection over union (mIoU) accuracy measures are improved by 0.82–11.83% and 0.74–7.5%, respectively, over those of other methods.

The timely detection of fishing activities supports the protection of water resources and aids law enforcement in fishing ban and shoreline inspection scenarios. Fishing rods are highly discriminative objects for identifying such activities; however, their slender shapes and varying orientations make feature extraction and precise localization difficult to achieve. The existing slender object detectors typically require oriented bounding-box supervision, resulting in high annotation costs and unstable angle regression. To address these issues, we propose a weakly supervised slender object detection network (WSSO-DETR) with a low annotation cost. It employs horizontal-box supervision and exploits angle relation consistency and scale invariance across multiview transformations, reducing the need for oriented box annotations. The scale and orientation estimated by the horizontal box branch are used to construct a long-axis prior, based on which an oriented object reference point generation module (OORPG) is designed. We further introduce a point set-based oriented decoder (PSOD) and an encoder–decoder feature fusion-based prediction head (E-DFFPH) to model the interactions among object-related point sets and attain improved localization performance. Experiments show that WSSO-DETR improves the mAP₅₀ metric by 2.5–15.3% on Slender COCO, by 1.1–4.8% on Fishing Detect, and by 1.7–4.9% on DOTA-v1.0, demonstrating its effectiveness and generalizability.