Journal
REMOTE SENSING
Volume 11, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/rs11101158
Keywords
convolutional neural networks; semantic labeling; context aggregation; channel attention; residual convolution; aerial images
Categories
Funding
- 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]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available