Rotation-invariant image retrieval using hidden Markov tree for remote sensing data

Congcong Miao; Yindi Zhao

doi:10.1117/12.2068889

18 November 2014 Rotation-invariant image retrieval using hidden Markov tree for remote sensing data

Congcong Miao, Yindi Zhao

Proceedings Volume 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V; 92631W (2014) https://doi.org/10.1117/12.2068889
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

The rapid increase in quantity of available remote sensing data brought an urgent need for intelligent retrieval techniques for remote sensing images. As one of the basic visual characteristics and important information sources of remote sensing images, texture is widely used in the scheme of remote sensing image retrieval. Since many images or regions with identical texture features usually show the diversity of direction, the consideration of rotation-invariance in the description of texture features is of significance both theoretically and practically. To address these issues, we develop a rotation-invariant image retrieval method based on the texture features of remote sensing images. We use the steerable pyramid transform to get the multi-scale and multi-orientation representation of texture images. Then we employ the hidden Markov tree (HMT) model, which provides a good tool to describe texture feature, to capture the dependencies across scales and orientations, by which the statistical properties of the transform domain coefficients can be obtained. Utilizing the inherent tree structure of the HMT and its fast training and likelihood computation algorithms, we can extract the rotation-invariant features of texture images. Similarity between the query image and each candidate image in the database can be measured by computing the Kullback-Leibler distance between the corresponding models. We evaluate the retrieval effectiveness of the algorithm with Brodatz texture database and remote sensing images. The experimental results show that this method has satisfactory performance in image retrieval and less sensitivity to texture rotation.

Citation Download Citation

Congcong Miao and Yindi Zhao "Rotation-invariant image retrieval using hidden Markov tree for remote sensing data", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92631W (18 November 2014); https://doi.org/10.1117/12.2068889

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KEYWORDS

Image retrieval

Remote sensing

Databases

Image resolution

Feature extraction

Data modeling

Image sensors

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