像素
计算机科学
增采样
特征(语言学)
人工智能
保险丝(电气)
卷积神经网络
网(多面体)
遥感
频道(广播)
模式识别(心理学)
高分辨率
计算机视觉
图像(数学)
数学
地理
电信
工程类
哲学
语言学
几何学
电气工程
作者
Lifan Zhou,Wenjie Xing,Jinshan Zhu,Yu Xia,Shan Zhong,Shengrong Gong
出处
期刊:IEEE journal on miniaturization for air and space systems
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:4 (4): 368-375
标识
DOI:10.1109/jmass.2023.3299330
摘要
High-resolution pixel-level classification of the roads and rivers in the remote sensing system has extremely important application value and has been a research focus which is received extensive attention from the remote sensing society. In recent years, deep convolutional neural networks (DCNNs) have been used in the pixel-level classification of remote sensing images, which has shown extraordinary performance. However, the traditional DCNNs mostly produce discontinuous and incomplete pixel-level classification results when dealing with thin-stripped roads and rivers. To solve the above problem, we put forward a high-resolution strong fusion network (abbreviated as HRSF-Net) which can keep the feature map at high resolution and minimize the texture information loss of the thin-stripped target caused by multiple downsampling operations. In addition, a pixel relationship enhancement and dual-channel attention (PRE-DCA) module is proposed to fully explore the strong correlation between the thin-stripped target pixels, and a hetero-resolution fusion (HRF) module is also proposed to better fuse the feature maps with different resolutions. The proposed HRSF-Net is examined on the two public remote sensing datasets. The ablation experimental result verifies the effectiveness of each module of the HRSF-Net. The comparative experimental result shows that the HRSF-Net has achieved mIoU of 79.05% and 64.46% on the two datasets, respectively, which both outperform some advanced pixel-level classification methods.
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