A Novel Content Based Hyperspectral Image Retrieval System Based on Bag of End Members

Date

2016-05-19

Author

Omruuzun, Fatih
Demir, Begum
Bruzzone, Lorenzo
Çetin, Yasemin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper proposes a bag of end members based novel method for content-based hyperspectral image retrieval. The proposed system exploits high resolution spectral signatures of the distinct materials in the hyperspectral images and consists of two steps. In the first step, query and archive hyperspectral images are represented with a feature vector computed by a novel bag of end members based feature extraction method. In the second step, the similarities between the feature vector of the query image and those of the archive images are initially computed by the use of histogram intersection kernel and then the most similar images are retrieved. The proposed system is tested on a hyperspectral image archive created from an EO-1 Hyperion hyperspectral sensor image. The experimental results show that the proposed system increases hyperspectral image retrieval performance.

Subject Keywords

Hyperspectral imaging, Content based retrieval, Feature extraction, Unmixing, Bag of end members

URI

https://hdl.handle.net/11511/53179

Conference Name

24th Signal Processing and Communication Application Conference (SIU)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

F. Omruuzun, B. Demir, L. Bruzzone, and Y. Çetin, “A Novel Content Based Hyperspectral Image Retrieval System Based on Bag of End Members,” presented at the 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53179.