GSM演进的增强数据速率
计算机科学
机制(生物学)
分割
人工智能
边缘增强
图像分割
模式识别(心理学)
图像增强
图像(数学)
物理
量子力学
作者
Long Chen,Zhiyuan Qu,Yao Zhang,Jingyang Liu,Ruwen Wang,Dezheng Zhang
标识
DOI:10.1109/jstars.2024.3357540
摘要
In recent years, remote sensing images(RSIs) have witnessed significant improvements in both quality and quantity. With the application of deep learning techniques, these RSIs can be more effectively utilized to harnessed to aid in environment monitoring and urban planning. Semantic segmentation, as a common task in RSIs processing, confronts numerous challenges, including inaccurate classification, fuzzy boundaries, and other problems. This paper proposes a novel semantic segmentation network known as the Edge Enhanced Global Contextual Information Guided Feature Fusion Network (Edge Enhanced GCIFFNet) to address these challenges. This network consists of an edge-enhanced part and a backbone network part. Firstly, in the encoding stage, the Recurrent Criss-Cross Attention (RCCA) block is employed, which incorporates spatial attention, mechanisms to capture global information. Secondly, in the decoding stage, a Channel Attention Residual Block (CARB) module is proposed to facilitate the fusion of high-level and low-level features. Moreover, we enhance the network's ability to extract edge information during training by sharing parameters between the backbone and employing a specialized loss function. The network proposed in this paper utilises both channel attention and spatial attention at different stages, effectively utilizing edge information. Finally, we conduct experiments using the Yinchuan dataset and the LoveDA dataset. The experimental results show that the proposed network demonstrates excellent performance on both datasets.
科研通智能强力驱动
Strongly Powered by AbleSci AI