Building extraction from VHR remote sensing imagery by combining an improved deep convolutional encoder-decoder architecture and historical land use vector map-Reference-Cited by-同舟云学术

Building extraction from VHR remote sensing imagery by combining an improved deep convolutional encoder-decoder architecture and historical land use vector map

Published:2020-06-17 Issue:17 Volume:41 Page:6595-6617
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Feng Wenqing¹,Sui Haigang¹²,Hua Li³,Xu Chuan¹,Ma Guorui¹,Huang Weiming⁴^ORCID

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University , Wuhan, China

2. Collaborative Innovation Center of Geospatial Technology, Wuhan University , Wuhan, China

3. College of Resources and Environment, Huazhong Agricultural University , Wuhan, China

4. Department of Physical Geography and Ecosystem Science, Lund University , Lund, Sweden

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1742944

Reference44 articles.

1. Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

2. Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks

3. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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