Sparse and smooth feature extraction for hyperspectral imagery

Behnood Rasti; Magnus O. Ulfarsson; Pedram Ghamisi

doi:10.1109/IGARSS.2018.8517672

Sparse and smooth feature extraction for hyperspectral imagery

Behnood Rasti, Magnus O. Ulfarsson, Pedram Ghamisi

Faculty of Electrical and Computer Engineering

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

1 Citation (Scopus)

Abstract

In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4760-4763
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8517672
Publication status	Published - 31 Oct 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE

Other keywords

Feature extraction
Hyperspectral image
Low-rank model
Regularization
Sparsity
Total variation

Access to Document

10.1109/IGARSS.2018.8517672

Cite this

Rasti, B., Ulfarsson, M. O., & Ghamisi, P. (2018). Sparse and smooth feature extraction for hyperspectral imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 4760-4763). Article 8517672 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8517672

@inproceedings{7686731c32564d0c8e13b46d945f4de6,

title = "Sparse and smooth feature extraction for hyperspectral imagery",

abstract = "In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.",

keywords = "Feature extraction, Hyperspectral image, Low-rank model, Regularization, Sparsity, Total variation",

author = "Behnood Rasti and Ulfarsson, {Magnus O.} and Pedram Ghamisi",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

year = "2018",

month = oct,

day = "31",

doi = "10.1109/IGARSS.2018.8517672",

language = "English",

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

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

pages = "4760--4763",

booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",

address = "United States",

}

Rasti, B, Ulfarsson, MO & Ghamisi, P 2018, Sparse and smooth feature extraction for hyperspectral imagery. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8517672, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 4760-4763, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8517672

Sparse and smooth feature extraction for hyperspectral imagery. / Rasti, Behnood; Ulfarsson, Magnus O.; Ghamisi, Pedram.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 4760-4763 8517672 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

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

TY - GEN

T1 - Sparse and smooth feature extraction for hyperspectral imagery

AU - Rasti, Behnood

AU - Ulfarsson, Magnus O.

AU - Ghamisi, Pedram

PY - 2018/10/31

Y1 - 2018/10/31

N2 - In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.

AB - In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.

KW - Feature extraction

KW - Hyperspectral image

KW - Low-rank model

KW - Regularization

KW - Sparsity

KW - Total variation

UR - http://www.scopus.com/inward/record.url?scp=85063161843&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2018.8517672

DO - 10.1109/IGARSS.2018.8517672

M3 - Conference contribution

AN - SCOPUS:85063161843

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 4760

EP - 4763

BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

Rasti B, Ulfarsson MO, Ghamisi P. Sparse and smooth feature extraction for hyperspectral imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 4760-4763. 8517672. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8517672

Sparse and smooth feature extraction for hyperspectral imagery

Abstract

Publication series

Conference

Bibliographical note

Other keywords

Access to Document

Fingerprint

Cite this