SRCBTFusion-Net: An Efficient Fusion Architecture via Stacked Residual Convolution Blocks and Transformer for Remote Sensing Image Semantic Segmentation-Reference-Cited by-同舟云学术

SRCBTFusion-Net: An Efficient Fusion Architecture via Stacked Residual Convolution Blocks and Transformer for Remote Sensing Image Semantic Segmentation

Published:2023 Issue: Volume:61 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Chen Junsong¹^ORCID,Yi Jizheng¹^ORCID,Chen Aibin¹,Lin Hui²

Affiliation:

1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China

2. Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Key Laboratory on Forest Resources Management and Monitoring in Southern China of National Forestry and Grassland Administration, Changsha, China

Funder

Hunan Provincial Natural Science Foundation of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10328787.pdf?arnumber=10328787

Reference46 articles.

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4. CAA: Channelized axial attention for semantic segmentation;huang;arXiv 2101 07434,2021

5. An opposition equilibrium optimizer for context-sensitive entropy dependency based multilevel thresholding of remote sensing images

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