Semantic Uncertainty‐Awared for Semantic Segmentation of Remote Sensing Images

IET Image Processing, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 1, 2025

ABSTRACT Remote sensing image segmentation is crucial for applications ranging from urban planning to environmental monitoring. However, traditional approaches struggle with the unique challenges of aerial imagery, including complex boundary delineation and intricate spatial relationships. To address these limitations, we introduce semantic uncertainty‐aware (SUAS) method, an innovative plug‐and‐play solution designed specifically remote analysis. SUAS builds upon rotated multi‐scale interaction network (RMSIN) architecture introduces prompt refinement uncertainty adjustment module (PRUAM). This novel component transforms original textual prompts into descriptions, particularly focusing on ambiguous boundaries prevalent in imagery. By incorporating uncertainty, directly tackles inherent complexities delineation, enabling more refined segmentations. Experimental results demonstrate SUAS's effectiveness, showing improvements over existing methods across multiple metrics. achieves consistent enhancements mean intersection‐over‐union (mIoU) precision at various thresholds, notable performance handling objects irregular boundaries—a persistent challenge imagery The indicate that design, which leverages guide task, contributes improved accuracy

Language: Английский

An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery

Chinese journal of information fusion., Journal Year: 2025, Volume and Issue: 2(1), P. 79 - 99

Published: March 29, 2025

Sea ice detection is of vital importance for maritime navigation. Satellite imagery a crucial medium conveying information about sea ice. Currently, most models mainly rely on texture to identify in satellite imagery, while ignoring size information. This research presents an improved YOLOv8-Based algorithm multi-scale First, we propose fusion module based the attention mechanism and use it replace Concat YOLOv8 network structure. Second, conduct applicability analysis bounding box regression loss function ultimately select Shape-IoU, which more suitable ice, as regression. Third, analyze distribution characteristics with different sizes NWPU-RESISC45 dataset. Based these characteristics, predicted by are converted into evidence vectors uncertainty quantification. Subsequently, achieved fusing probability categories. Compared other algorithm, our achieves better accuracy both Landsat-8-derived Ice datasets.

Language: Английский

Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Language: Английский