TDSCCNet: twin-depthwise separable convolution connect network for change detection with heterogeneous images-Reference-Cited by-同舟云学术

TDSCCNet: twin-depthwise separable convolution connect network for change detection with heterogeneous images

Published:2024-01 Issue:1 Volume:39 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Wang Min¹,Huang Liang¹²,Tang Bo-Hui¹²³,Le Weipeng¹,Tian Qiuyuan¹

Affiliation:

1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, China

2. Key Laboratory of Plateau Remote Sensing, Yunnan Provincial Department of Education, Kunming, China

3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Yunnan Fundamental Research Project

Hunan Provincial Natural Science Foundation of China

‘Xingdian’ talent support program project and Yunnan Province key research and development program

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2024.2329673

Reference50 articles.

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3. Chollet F. 2016. Xception: deep learning with depthwise separable convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); p. 1800–1807. doi: 10.1109/CVPR.2017.195.

4. Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes