PiCovS: Pixel-Level With Covariance Pooling Feature and Superpixel-Level Feature Fusion for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

PiCovS: Pixel-Level With Covariance Pooling Feature and Superpixel-Level Feature Fusion for Hyperspectral Image Classification

Published:2023 Issue: Volume:61 Page:1-20
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Nartey Obed Tettey¹^ORCID,Sarpong Kwabena¹^ORCID,Addo Daniel¹^ORCID,Rao Yunbo¹^ORCID,Qin Zhiguang¹^ORCID

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

Funder

National Natural Science Foundation of China, Major Instrument Project the Development of Fetal Heart Sound-Cardiac-Ultrasound Multimodal Auxiliary Diagnostic Equipment

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10273416.pdf?arnumber=10273416

Reference61 articles.

1. Deep Fully Convolutional Network-Based Spatial Distribution Prediction for Hyperspectral Image Classification

2. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

3. Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

4. 3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification

5. Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network

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