CONTENT BASED HYPERSPECTRAL IMAGE RETRIEVAL USING BAG OF ENDMEMBERS IMAGE DESCRIPTORS

Date

2016-08-24

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 novel system for fast and accurate content based retrieval of hyperspectral images. The proposed system aims at retrieving hyperspectral images that have both similar spectral characteristics associated with specific materials and fractional abundances to the query image. It consists of two modules. The first module characterizes the query and the target hyperspectral images in the archive by two descriptors: 1) a binary spectral descriptor representing spectral characteristics of distinct materials 2) an abundance descriptor that contains the normalized cumulative fractional abundance information of the corresponding materials. Both descriptors are obtained by a novel bag of endmembers based strategy. The second module aims at retrieving hyperspectral images from the archive that are most similar to query image based on a hierarchical strategy which evaluates the spectral and abundance descriptors similarity. Experiments carried out on a benchmark archive of hyperspectral images demonstrated the effectiveness of the proposed system in terms of retrieval accuracy and time.

Subject Keywords

Hyperspectral imaging, Content based image retrieval, Feature extraction, Unmixing, Remote sensing, Bag of endmembers

URI

https://hdl.handle.net/11511/55484

Conference Name

8th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

F. Omruuzun, B. Demir, L. Bruzzone, and Y. Çetin, “CONTENT BASED HYPERSPECTRAL IMAGE RETRIEVAL USING BAG OF ENDMEMBERS IMAGE DESCRIPTORS,” presented at the 8th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55484.