CONTENT BASED HYPERSPECTRAL IMAGE RETRIEVAL USING BAG OF ENDMEMBERS IMAGE DESCRIPTORS

Date

2016-08-24

Author

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

Metadata

Show full item record

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

Item Usage Stats

170
views

0
downloads

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

Suggestions

OpenMETU
Core

A Novel Content Based Hyperspectral Image Retrieval System Based on Bag of End Members Omruuzun, Fatih; Demir, Begum; Bruzzone, Lorenzo; Çetin, Yasemin (2016-05-19) 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 th...
Content-Based Retrieval of Audio in News Broadcasts Dogan, Ebru; SERT, MUSTAFA; Yazıcı, Adnan (2009-10-28) This paper describes a complete, scalable and extensible content-based retrieval system for news broadcasts. Depending on segmentation results of the selected audio data, our system allows users to query audio data semantically by using both domain based fuzzy classes (anchor, commercial, reporter, sports, transition, weatherforecast, and venuesound) and similarity search. Two kinds of experiments were conducted on audio tracks of TRECVID news broadcasts to evaluate performance of the proposed query-by-exam...
Content Based Copy Detection with Coarse Audio-Visual Fingerprints Saracoglu, Ahmet; Esen, Ersin; Ates, Tugrul K.; Acar, Banu Oskay; Zubari, Uenal; Ozan, Ezgi C.; Ozalp, Egemen; Alatan, Abdullah Aydın; Ciloglu, Tolga (2009-01-01) Content based copy detection (CBCD) emerges as a viable choice against active detection methodology of watermarking. The very first reason is that the media already under circulation cannot be marked and secondly, CBCD inherently can endure various severe attacks, which watermarking cannot. Although in general, media content is handled independently as visual and audio in this work both information sources are utilized in a unified framework, in which coarse representation of fundamental features are employ...
Road network extraction from high-resolution multi spectral satellite images Karaman, Ersin; Çetin, Yasemin; Department of Information Systems (2012) In this thesis, an automatic road extraction algorithm for multi-spectral images is developed. The developed model extracts elongated structures from images by using edge detection, segmentation and clustering techniques. The study also extracts non-road regions like vegetative fields, bare soils and water bodies to obtain more accurate road map. The model is constructed in a modular approach that aims to extract roads with different characteristics. Each module output is combined to create a road score map...
Attention based image retrieval Özyer, Gülşah Tümüklü; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2013) This thesis proposes a content-based image retrieval (CBIR) system based on the human visual attention, called Attention-based Image Retrieval (ABIR). The proposed ABIR system handles CBIR problem from the perspective of human perception. An efficient visual attention model specific to CBIR problem derived from the computational visual attention model of Itti and Koch is suggested. ABIR system defines the CBIR system as an attention task, where query and images in the database are considered together to ext...

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.