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Proceedings Paper

Two-stage high resolution remote sensing image retrieval combining semantic and visual features
Author(s): Qi-Ming Wan; Min Wang; Xing-Yue Zhang; Da-Qian Zhang
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Paper Abstract

In this work, we put forward a two-stage image retrieval methodology by integrating high level image semantic features and low level visual features. At the first stage, we segment an image into parcels using a multiresolution remotely sensed image segmentation method combining rainfalling watershed algorithm and fast region merging. We then classify these parcels with Support Vector Machine (SVM), a famous non-linear classification scheme to connect the low-level visual features with high-level semantic features. These classes are then stored in semantic features databases for future use. When users carry out their rough semantic retrieval, they should choose and combine these semantic classes, and our method returns some image blocks which include the interested classes as the first "rough" retrieval results. At the second stage users should select an example from the results. We then construct and compare the similarity between the color and texture histograms for both the query example and each one in the semantic retrieval result. If the total similarity is higher than some threshold, the image will be returned as a suitable retrieval result. These images are sorted according their similarity as the final retrieval results. Experiments indicate our approach can get more effective and accurate results than content-based image retrieval only using visual features.

Paper Details

Date Published: 30 October 2009
PDF: 11 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749550 (30 October 2009); doi: 10.1117/12.832727
Show Author Affiliations
Qi-Ming Wan, Nanjing Normal Univ. (China)
Min Wang, Nanjing Normal Univ. (China)
Xing-Yue Zhang, Nanjing Normal Univ. (China)
Da-Qian Zhang, Nanjing Normal Univ. (China)

Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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