Zhang, H.; Xie, G. HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation. Preprints2023, 2023101577. https://doi.org/10.20944/preprints202310.1577.v1
APA Style
Zhang, H., & Xie, G. (2023). HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation. Preprints. https://doi.org/10.20944/preprints202310.1577.v1
Chicago/Turabian Style
Zhang, H. and Gang Xie. 2023 "HRFNet: A Novel Hierarchical Rich-scale Fusion Network for Remote Sensing Semantic Segmentation" Preprints. https://doi.org/10.20944/preprints202310.1577.v1
Abstract
The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
