The purpose of this paper is to improve the retrieval results of hyperspectral image by integrating both spectral and textural features. For this purpose, an improved multiscale opponent representation for hyperspectral texture is proposed to represent the spatial information of the hyperspectral scene.
In the presented approach, end-member signatures are extracted as spectral features by means of the widely used end-member induction algorithm N-FINDR, and the improved multiscale opponent representation is extracted from the first three principal components of the hyperspectral data based on Gabor filters. Then, the combination similarity between query image and other images in the database is calculated, and the first k more similar images are returned in descending order of the combination similarity.
Some experiments are calculated using the airborne hyperspectral data of Washington DC Mall. According to the experimental results, the proposed method improves the retrieval results, especially for image categories that have regular textural structures.
The paper presents an effective retrieval method for hyperspectral images.
The authors would like to thank the anonymous reviewers for their comments and suggestions. This work was supported by National Science & Technology Specific Projects [grant number 2012YQ16018505] and National Natural Science Foundation of China [grant number 61172174].
Shao, Z., Zhou, W., Cheng, Q., Diao, C. and Zhang, L. (2015), "An effective hyperspectral image retrieval method using integrated spectral and textural features", Sensor Review, Vol. 35 No. 3, pp. 274-281. https://doi.org/10.1108/SR-10-2014-0716Download as .RIS
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