Bin~Wan,G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images

2026-03-13

AI Summary

This work addresses the challenges of salient object detection in optical remote sensing imagery, where large scale variations and complex backgrounds hinder existing methods from effectively modeling geometric structures and fine-grained details, often yielding incomplete detection results. To overcome these limitations, the authors propose G2HFNet, a novel architecture built upon the Swin Transformer backbone that introduces, for the first time, a geometry-granularity-aware mechanism. The model incorporates a hierarchical feature fusion framework comprising four key components: Multi-scale Detail Enhancement (MDE), Dual-branch Geometry-Granularity Complementary (DGC), Deep Semantic Perception (DSP), and Local-Global Guided Fusion (LGF). Extensive experiments demonstrate that G2HFNet significantly outperforms state-of-the-art methods across multiple remote sensing datasets, particularly excelling in complex scenes by producing more complete and accurate saliency maps.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Remote sensing images captured from aerial perspectives often exhibit significant scale variations and complex backgrounds, posing challenges for salient object detection (SOD). Existing methods typically extract multi-level features at a single scale using uniform attention mechanisms, leading to suboptimal representations and incomplete detection results. To address these issues, we propose a GeoGran-Aware Hierarchical Feature Fusion Network (G2HFNet) that fully exploits geometric and granular cues in optical remote sensing images. Specifically, G2HFNet adopts Swin Transformer as the backbone to extract multi-level features and integrates three key modules: the multi-scale detail enhancement (MDE) module to handle object scale variations and enrich fine details, the dual-branch geo-gran complementary (DGC) module to jointly capture fine-grained details and positional information in mid-level features, and the deep semantic perception (DSP) module to refine high-level positional cues via self-attention. Additionally, a local-global guidance fusion (LGF) module is introduced to replace traditional convolutions for effective multi-level feature integration. Extensive experiments demonstrate that G2HFNet achieves high-quality saliency maps and significantly improves detection performance in challenging remote sensing scenarios.

Problem

• salient object detection
• optical remote sensing images
• scale variations
• complex backgrounds
• multi-level feature representation

Innovation

• GeoGran-Aware
• Hierarchical Feature Fusion
• Swin Transformer
• Multi-scale Detail Enhancement
• Local-Global Guidance Fusion