Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification

Feiyan Li; Wei Li; Hongtao Huo; Qiong Ran

doi:10.1109/IGARSS.2019.8897839

Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification

Feiyan Li, Wei Li, Hongtao Huo^*, Qiong Ran

^*Corresponding author for this work

School of Information and Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	401-404
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8897839
Publication status	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

Keywords

Decision fusion
Hyperspectral Imagery
Pattern Classification
Sparse and low rank matrix decomposition

Access to Document

10.1109/IGARSS.2019.8897839

Cite this

Li, F., Li, W., Huo, H., & Ran, Q. (2019). Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 401-404). Article 8897839 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8897839

Li, Feiyan ; Li, Wei ; Huo, Hongtao et al. / Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification. 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 401-404 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{e9de86c0c8bf40e38f1e9a21c90ae4aa,

title = "Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification",

abstract = "Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.",

keywords = "Decision fusion, Hyperspectral Imagery, Pattern Classification, Sparse and low rank matrix decomposition",

author = "Feiyan Li and Wei Li and Hongtao Huo and Qiong Ran",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

month = jul,

doi = "10.1109/IGARSS.2019.8897839",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "401--404",

booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",

address = "United States",

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Li, F, Li, W, Huo, H & Ran, Q 2019, Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8897839, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 401-404, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8897839

Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification. / Li, Feiyan; Li, Wei; Huo, Hongtao et al.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 401-404 8897839 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification

AU - Li, Feiyan

AU - Li, Wei

AU - Huo, Hongtao

AU - Ran, Qiong

PY - 2019/7

Y1 - 2019/7

N2 - Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.

AB - Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.

KW - Decision fusion

KW - Hyperspectral Imagery

KW - Pattern Classification

KW - Sparse and low rank matrix decomposition

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DO - 10.1109/IGARSS.2019.8897839

M3 - Conference contribution

AN - SCOPUS:85077700928

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 401

EP - 404

BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings

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Li F, Li W, Huo H, Ran Q. Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 401-404. 8897839. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8897839

Decision Fusion Based on Joint Low Rank and Sparse Component for Hyperspectral Image Classification

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