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A distributed system architecture for high-resolution remote sensing image retrieval by combining deep and traditional features
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Paper Abstract

The recent advance of satellite technology has led to explosive growth of high-resolution remote sensing images in both quantity and quality. To address the challenges of high-resolution remote sensing images retrieval in both efficiency and accuracy, a distributed system architecture for satellite images retrieval by combining deep and traditional hand-crafted features is proposed in this paper. On one hand, to solve the problem of higher computational complexity and storage capacity, Hadoop framework is applied to manage satellite image data and to extract image features in parallel environment. On the other hand, deep features based on convolutional neural networks (CNNs) are extracted and combined with traditional features to overcome the limitations of hand-crafted features. Besides, object detection are integrated in the proposed system to realize accurate object locating at the time of retrieval. Experiments are carried on several challenging datasets to evaluate the performance of the proposed distributed system. Standard metrics like retrieval precision, recall and computing time under different configurations are compared and analyzed. Experimental results demonstrate that our system architecture is practical and feasible, both efficiency and accuracy can meet realistic demands.

Paper Details

Date Published: 9 October 2018
PDF: 20 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 1078918 (9 October 2018); doi: 10.1117/12.2323310
Show Author Affiliations
Qimin Cheng, Huazhong Univ. of Science and Technology (China)
Kang Shao, Huazhong Univ. of Science and Technology (China)
Chengyuan Li, Wuhan Univ. (China)
Sen Li, Huazhong Univ. of Science and Technology (China)
Jinling Li, Huazhong Univ. of Science and Technology (China)
Zhenfeng Shao, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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