Learning Multiscale Temporal–Spatial–Spectral Features via a Multipath Convolutional LSTM Neural Network for Change Detection With Hyperspectral Images-Reference-Cited by-同舟云学术

Learning Multiscale Temporal–Spatial–Spectral Features via a Multipath Convolutional LSTM Neural Network for Change Detection With Hyperspectral Images

Published:2022 Issue: Volume:60 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:

Shi Changjiang¹^ORCID,Zhang Zhijie²^ORCID,Zhang Wanchang¹^ORCID,Zhang Chuanrong²^ORCID,Xu Qiang³^ORCID

Affiliation:

1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

2. Department of Geography, University of Connecticut, Storrs, CT, USA

3. State Key Laboratory of Geohazard Prevention and Geo-Environment Protection, Chengdu University of Technology, Chengdu, China

Funder

Key Research and Development and Transformation Program of Qinghai Province

National Key Research and Development Program 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/ielx7/36/9633014/09779153.pdf?arnumber=9779153

Reference56 articles.

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3. Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

4. Multi-scale 3D deep convolutional neural network for hyperspectral image classification

5. CBAM: Convolutional block attention module;woo;Proc Eur Conf Comput Vis (ECCV),2018

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