Combine reflectance with shading component for hyperspectral image classification

Xudong Jin; Yanfeng Gu

doi:10.1109/IGARSS.2018.8518197

Combine reflectance with shading component for hyperspectral image classification

Xudong Jin
, Yanfeng Gu

School of Electronics and Information Engineering

Harbin Institute of Technology

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

3 Scopus citations

Abstract

Intrinsic image decomposition (IID) of hyperspectral images (HSIs) aims to separate the reflectance cube and shading component from the original image data. The reflectance cube contains the spectral information reflecting the intrinsic properties of the material, whereas the shading component contains the spatial information reflecting geometric structure of the object like the surface orientation changes. From the perspective of hyperspectral image classification, combining spectral information with spatial information can be useful for improving the classification performance. In this paper, a new optimization algorithm is proposed for intrinsic image decomposition of hyperspectral images, and composite kernel learning (CKL) method is further utilized to combine reflectance with shading component.

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	9-12
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8518197
State	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

Keywords

Classification
Hyperspectral images (HSIs)
Intrinsic image decomposition (IID)
Shading

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10.1109/IGARSS.2018.8518197

Cite this

Jin, X., & Gu, Y. (2018). Combine reflectance with shading component for hyperspectral image classification. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 9-12). Article 8518197 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8518197

@inproceedings{28aa711fef2e49d9ab9bfa9ee3245af7,

title = "Combine reflectance with shading component for hyperspectral image classification",

abstract = "Intrinsic image decomposition (IID) of hyperspectral images (HSIs) aims to separate the reflectance cube and shading component from the original image data. The reflectance cube contains the spectral information reflecting the intrinsic properties of the material, whereas the shading component contains the spatial information reflecting geometric structure of the object like the surface orientation changes. From the perspective of hyperspectral image classification, combining spectral information with spatial information can be useful for improving the classification performance. In this paper, a new optimization algorithm is proposed for intrinsic image decomposition of hyperspectral images, and composite kernel learning (CKL) method is further utilized to combine reflectance with shading component.",

keywords = "Classification, Hyperspectral images (HSIs), Intrinsic image decomposition (IID), Shading",

author = "Xudong Jin and Yanfeng Gu",

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.2018.8518197",

language = "英语",

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

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

pages = "9--12",

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

address = "美国",

}

Jin, X & Gu, Y 2018, Combine reflectance with shading component for hyperspectral image classification. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518197, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 9-12, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518197

Combine reflectance with shading component for hyperspectral image classification. / Jin, Xudong; Gu, Yanfeng.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 9-12 8518197 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

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

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AU - Gu, Yanfeng

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N2 - Intrinsic image decomposition (IID) of hyperspectral images (HSIs) aims to separate the reflectance cube and shading component from the original image data. The reflectance cube contains the spectral information reflecting the intrinsic properties of the material, whereas the shading component contains the spatial information reflecting geometric structure of the object like the surface orientation changes. From the perspective of hyperspectral image classification, combining spectral information with spatial information can be useful for improving the classification performance. In this paper, a new optimization algorithm is proposed for intrinsic image decomposition of hyperspectral images, and composite kernel learning (CKL) method is further utilized to combine reflectance with shading component.

AB - Intrinsic image decomposition (IID) of hyperspectral images (HSIs) aims to separate the reflectance cube and shading component from the original image data. The reflectance cube contains the spectral information reflecting the intrinsic properties of the material, whereas the shading component contains the spatial information reflecting geometric structure of the object like the surface orientation changes. From the perspective of hyperspectral image classification, combining spectral information with spatial information can be useful for improving the classification performance. In this paper, a new optimization algorithm is proposed for intrinsic image decomposition of hyperspectral images, and composite kernel learning (CKL) method is further utilized to combine reflectance with shading component.

KW - Classification

KW - Hyperspectral images (HSIs)

KW - Intrinsic image decomposition (IID)

KW - Shading

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M3 - 会议稿件

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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 -

Combine reflectance with shading component for hyperspectral image classification

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