期刊
REMOTE SENSING
卷 11, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/rs11101158
关键词
convolutional neural networks; semantic labeling; context aggregation; channel attention; residual convolution; aerial images
类别
资金
- National Natural Science Foundation of China (NSFC) [61771351]
- CETC key laboratory of aerospace information applications [SXX18629T022]
- project for innovative research groups of the natural science foundation of Hubei Province [2018CFA006]
Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. It is widely used in land-use surveys, change detection, and environmental protection. Recent researches reveal the superiority of Convolutional Neural Networks (CNNs) in this task. However, multi-scale object recognition and accurate object localization are two major problems for semantic labeling methods based on CNNs in high resolution aerial images. To handle these problems, we design a Context Fuse Module, which is composed of parallel convolutional layers with kernels of different sizes and a global pooling branch, to aggregate context information at multiple scales. We propose an Attention Mix Module, which utilizes a channel-wise attention mechanism to combine multi-level features for higher localization accuracy. We further employ a Residual Convolutional Module to refine features in all feature levels. Based on these modules, we construct a new end-to-end network for semantic labeling in aerial images. We evaluate the proposed network on the ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate that our network outperforms other competitors on both datasets with only raw image data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据