Paper Info
Title
End-to-End DSM Fusion Networks for Semantic Segmentation in High-Resolution Aerial Images
Abstract
Semantic segmentation in high-resolution aerial images is a fundamental research problem in remote sensing field for its wide range of applications. However, it is difficult to distinguish regions with similar spectral features using only multispectral data. Recent research studies have indicated that the introduction of multisource information can effectively improve the robustness of segmentation method. In this letter, we use digital surface models (DSMs) information as a complementary feature to further improve the semantic segmentation results. To this end, we propose a lightweight and simple DSM fusion (DSMF) branch structure module. Compared with the existing feature extraction structures, proposed DSMF module is simple and can be easily applied to other networks. In addition, we investigate four fusion strategies based on DSMF module to explore the optimal feature fusion strategy and four end-to-end DSMFNets are designed according to the corresponding strategies. We evaluate our models on International Society for Photogrammetry and Remote Sensing Vaihingen data set and all DSMFNets achieve promising results. In particular, DSMFNet-1 achieves an overall accuracy of 91.5% on the test data set.
Year
DOI
Venue
2019
10.1109/LGRS.2019.2907009
IEEE Geoscience and Remote Sensing Letters
Keywords
Field
DocType
Convolutional neural networks (CNNs),deep learning,high-resolution aerial images,semantic segmentation
Photogrammetry,Computer vision,End-to-end principle,Segmentation,Fusion,Feature extraction,Robustness (computer science),Artificial intelligence,Test data,Deep learning,Mathematics
Journal
Volume
Issue
ISSN
16
11
1545-598X
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Zhiying Cao100.34
Kun Fu241457.81
Xiaode Lu300.34
Wenhui Diao44312.16
Hao Sun514011.86
Menglong Yan6181.67
Hongfeng Yu703.72
Xian Sun8165.49