作者:Peng, FF (Peng, Feifei); Wang, L (Wang, Le); Gong, JY (Gong, Jianya); Wu, HY (Wu, Huayi)

卷:8 期:2 页:800-815 DOI:10.1109/JSTARS.2014.2363953 出版年:FEB 2015

摘要:The wide availability and increasing number of applications for high-resolution optical satellite stereo images (HrosSIs) have created a surging demand for the development of effective content-based image retrieval methods. However, this is a challenge for existing stereo image retrieval methods since they were designed for stereo images collected from close-range imaging sensors. Thus, successful retrieval of images is not assured given the mismatch between existing methods and the characteristics of HrosSIs. Moreover, none of the existing remote sensing image retrieval methods takes account of the specific characteristics of HrosSIs such as the viewing number and multiview angles. This paper proposes a generic framework to exploit the unique characteristics of HrosSIs data so as to allow efficient and accurate content-based HrosSI retrieval. HrosSIs retrieval is executed by similarity matching between the features obtained from digital surface models (DSMs) and orthoimages, both extracted from the HrosSIs. In addition, the significance of height information for HrosSI retrieval was investigated. A prototype system was designed and implemented for method validation using the ISPRS stereo benchmark test dataset. Experimental results show that the proposed techniques are efficient for HrosSI retrieval. The proposed framework is efficient and suitable for spaceborne stereo images but might also be suitable for airborne stereo images as well. Experimental results also show that height information alone is inefficient and unstable for HrosSI retrieval; however, a combination of height information and planar information is efficient and stable.

地址:[Peng, Feifei; Gong, Jianya; Wu, Huayi] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

[Wang, Le] SUNY Buffalo, Dept Geog, Buffalo, NY 14261 USA.

通讯作者地址:Peng, FF (通讯作者),Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

电子邮件地址:feifpeng@whu.edu.cn; lewang@buffalo.edu; gongjy@whu.edu.cn; wuhuayi@whu.edu.cn