计算机科学
背景(考古学)
分割
人工智能
图像分割
交叉口(航空)
航程(航空)
模式识别(心理学)
计算机视觉
生物
工程类
航空航天工程
古生物学
复合材料
材料科学
作者
Guangjun He,Zhe Dong,Pengming Feng,Dilxat Muhtar,Xueliang Zhang
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:20: 1-5
被引量:2
标识
DOI:10.1109/lgrs.2023.3233979
摘要
Although introducing self-attention mechanisms is beneficial to establish long-range dependencies and explore global context information in the task of remote sensing image semantic segmentation, it results in expensive computation and large memory cost. In this letter, we address this dilemma by proposing a lightweight dual-range context aggregation network (LDCANet) for efficient remote sensing image semantic segmentation. First, a dual-range context aggregation module (DCAM) is designed to aggregate the local features and the global semantic context acquired by convolutions and self-attention, respectively, where self-attention is implemented easily by applying two cascaded linear layers to reduce the computational complexity. Furthermore, a simple and lightweight decoder is employed to combine information from different levels, in which a multilayer perceptron (MLP)-based efficient linear block (ELB) is proposed to yield a strong and efficient representation. Experiments conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen dataset and the Gaofen Image dataset (GID) prove that our LDCANet achieves an excellent trade-off between segmentation accuracy and computational efficiency. In particular, our method achieves 74.12% mean intersection over union (mIoU) on the ISPRS Vaihingen dataset and 61.42% mIoU on the GID with only 4.98-M parameter size.
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