CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking-Reference-Cited by-同舟云学术

CBFF-Net: A New Framework for Efficient and Accurate Hyperspectral Object Tracking

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Gao Long¹,Liu Pan¹^ORCID,Jiang Yan²^ORCID,Xie Weiying¹^ORCID,Lei Jie¹^ORCID,Li Yunsong¹^ORCID,Du Qian³^ORCID

Affiliation:

1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

2. Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, U.K

3. Department of Electronic and Computer Engineering, Mississippi State University, Starkville, MS, USA

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10061231.pdf?arnumber=10061231

Reference73 articles.

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2. Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression

3. Unsupervised deep hyperspectral video target tracking and high spectral-spatial–temporal resolution (H3) benchmark dataset;liu;IEEE Trans Geosci Remote Sens,2021

4. Spectral curve fitting for automatic hyperspectral data analysis

5. BS-SiamRPN: Hyperspectral Video Tracking based on Band Selection and the Siamese Region Proposal Network

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