A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images-Reference-Cited by-同舟云学术

A Self-Learning-Update CNN Model for Semantic Segmentation of Remote Sensing Images

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Zheng Chen¹^ORCID,Hu Chen²,Chen Yuncheng²,Li Jingying²

Affiliation:

1. School of Mathematics and Statistics and the Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan Institute of Applied Mathematics, Henan University, Kaifeng, China

2. School of Mathematics and Statistics, Henan University, Kaifeng, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Henan

Youth Key Teacher of Henan

Scientific and Technological Innovation Talent of Henan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10081029.pdf?arnumber=10081029

Reference23 articles.

1. Dynamic Multi-Scale Filters for Semantic Segmentation

2. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

3. An MRF-Based Multigranularity Edge-Preservation Optimization for Semantic Segmentation of Remote Sensing Images

4. Fully convolutional networks for semantic segmentation

5. Context Encoding for Semantic Segmentation

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