JCSE, vol. 8, no. 2, pp.65-77, June, 2014

DOI: http://dx.doi.org/10.5626/JCSE.2014.8.2.65

An Object-Level Feature Representation Model for the Multi-target Retrieval of Remote Sensing Images

Zhi Zeng, Zhenhong Du and Renyi Liu
Huizhou University, Computer Science / Zhejiang University, Earth Science

Abstract: To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

Keyword: Remote sensing; Image processing; Spatial representation; 9DLT; Content-based remote sensing image

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