4.7 Article

Building Extraction in Very High Resolution Imagery by Dense-Attention Networks

Journal

REMOTE SENSING
Volume 10, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/rs10111768

Keywords

building extraction; deep learning; attention mechanism; very high resolution; imagery

Funding

  1. National Natural Science Foundation of China [41501376, 41571400]
  2. Natural Science Foundation of Anhui Province [1608085MD83]
  3. Key Laboratory of Earth Observation and Geospatial Information Science of NASG [201805]
  4. Science Research Project of Anhui Education Department [KJ2018A0007]
  5. open fund for Discipline Construction, Institute of Physical Science and Information Technology, Anhui University

Ask authors/readers for more resources

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder-decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red-green-blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available