With the increasingly improved earth observation systems, the rapid development of various observation techniques and computer intelligence technology, more and more research fields such as environmental monitoring, resource management, disaster forecast, major projects management, national security are highly rely on remote sensing information and its high timeliness. Therefore, we are not only facing the most abundant resources of remote sensing data, but also facing the challenges of transforming the data into information with effective information extraction technology. The Content-based Remote Sensing Image Retrieval (CBRSIR) as one effect approach that obtains remote sensing information quickly is receiving increased attention in the remote sensing application community, and it is emerging as a major research direction. Based on reviewing and analyzing the related technology on CBRSIR, the main bottleneck problems in current research scope are summarized in this paper, which are: one scene remote sensing image always has no main theme or obvious content; difficulty in indexing the multi-feature extracted from remote sensing image; it is difficult to apply multiple features synthetically; the current systems always have low intelligence and efficiency. These problems affect the development of CBRSR. Considering the diversity, complexity and mass of remote sensing images, this paper proposed an intelligent multi-feature integrated remote sensing image retrieval model and constructed its framework. Then the key technology of the multi-feature integrated model, the multi-feature description model, the theoretical framework of intelligent extraction methods and processing algorithms of the multi-feature are also proposed in this paper, and the feature research trend on multi-feature selection and dimensionality reduction, the multi-feature integrated similarity measure model, the intelligent feedback model are also prospected. In the end, this paper discussed the significance of construction of the intelligent multi-feature integrated remote sensing image retrieval method and the main feature work.
[1] 李德仁.论21世纪遥感与GIS的发展[J]. 武汉大学学报信息科学版,2003,28(2):127-130.
[2] 周明全,耿国华,韦娜. 基于内容图像检索技术[M].北京:清华大学出版社,2007,1-174.
[3] Flickner M,Sawney H, Niblack W, Ashley J, et al. Query by Image and Video Content: The QBIC System[J]. IEEE Computer, 1995, 28(9):23-32.
[4] Niblack W, Barber R, Equitz W, et al. The QBIC Project: Querying Images by Content Using Color, Texture, and Shape . Proc. of SPIE, Storage and Retrieval for Image and Video Databases, February 1993.
[5] Equitz W and Niblack W. Retrieving Images from a Database Using Texture-algorithms from the QBIC System . IBM Comput. Sci, Tech. Rep. RJ 9805, May 1994.
[6] Lee D, Barber R, Niblack W, M Flickner, J Hafner and D Petkovic. Indexing for Complex Queries on a Query-by-content Image Database . Proc. of the 12nd IAPR Int Conf on Pattern Recognition, Jerusalem, Israel, 1994,1:142-146.
[7] Dowe J. Content-based Retrieval in Multimedia Imaging . Proc. SPIE Storage and Retrieval for Image and Video Database, 1993,1908:164-167.
[8] Pentland A, Picard R W and Sclaroff S. Photobook:Content-based Manipulation of Image Databases[J]. Int. Journal of Computer Vision, 1996,18(3): 233-254.
[9] Pentland A, Picard R W and Sclaroff S. Photobook:Tools for Content-based Manipulation of Image Databases . Proceedings of the Symposium on Electronic Image: Science and Technology Storage and Retrieval for Image and Video Database II, SPIE, 1994, 2185: 34-47.
[10] Mehrotra S, Rui Y, Ortega M, and Huang T S. Supporting Content Based Queries over Images in Mars . Proc. IEEE Int. Conf. Multimedia Computing and Systems, 1997, 632-633.
[11] Rui Y, Huang T S and Mehrotra S. A Relevance Feedback Architecture in Content-based Multimedia Information Retrieval Systems . Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, Puerto Rico, June 20, 1997.
[12] 章毓晋.基于内容的视觉信息检索[M].北京:科学出版社,2003, 1-30.
[13] 姜秀华,王玉霞,陈旭灿.基于内容的检索技术与系统实现[J].北京广播学院学报(自然科学版),2003,10(1):18-25.
[14] 许营坤,王崇骏,杨育彬,等. 基于内容的图像检索系统的设计与实现[J].计算机科学,2004,31(2):139-140.
[15] 苗凤君,郭清宇,潘磊.基于内容图像检索技术的超声图像[J].中原工业院学报,2005,16(3):12-15.
[16] 林铭德,王加阳.基于内容的图像数据库检索原型系统的设计[J].福建电脑,2008(10):148-149.
[17] 闫实,付佳.基于内容特征的图像数据库检索技术及实现[J].计算机系统应用,2009,2:129-131.
[18] 徐长勇,周焰,李德仁. 基于内容的遥感图像检索综述[J].武汉理工大学学报,2003(10):8-12.
[19] 吴洪,卢汉清,马颂德.基于内容图像检索中相关反馈技术的回顾[J].计算机学报,2005,28(12):1970-1979.
[20] Bergman L D, Castelli V and Li Chung-Sheng. Progressive Content-Based Retrieval from Satellite Image Archives[J]. D-Lib Magazine, October 1997. http://www.dlib.org/dlib/october97/ibm/10li.html
[21] Stefanidis A, Agouris P. Sketch-based Image Retrieval in an Integrated GIS Environments . IAPRS, Stuttgart, 1998,32(4):597-603.
[22] Zhu B, Ramsey M, et al. Creating a Large-scale Content-based Airphoto Image Digital Library . Proceedings of IEEE Transaction on Image Processing, 2000,9(1):163-167.
[23] Priti M and Namita S. Retrieval of Remote Sensing Images Using Colour & Texture Attribute[J]. International Journal of Computer Science and Information Security, 2009,4(1).
[24] Priti M and Namita S. Prototype System for Retrieval of Remote Sensing Image Based on Color Moment and Gray Level Co-occurrence Matrix[J]. International Journal of Computer Science Issues, 2009, 3:20-23.
[25] 李德仁,宁晓刚.一种新的基于内容遥感图像检索的图像分块策略[J]. 武汉大学学报信息科学版,2006,31(8):659-662.
[26] 陆丽珍,刘仁义,刘南.一种融合颜色和纹理特征的遥感图像检索方法[J].中国图象图形学报,2004,9(3):328-333.
[27] 杜培军,唐宏,方涛.基于内容的遥感影像检索若干问题的研究[J].中国矿业大学学报,2005,34(3):270-273.
[28] 杜培军,陈云浩,方涛,等.基于光谱特征的高光谱遥感影像检索[J].光谱学与光谱分析,2005,25(8):1171-1175.
[29] 张成刚,毕建涛,池天河.遥感影像内容的语义查询算法与应用[J].地球信息科学,2007,9(3):109-115.
[30] 杜冲,司望利,许珺.基于地理语义的空间关系查询和推理[J].地球信息科学学报,2010,12(1):48-55.
[31] 舒飞跃,闾国年,陆婧,等. 基于知识对象的土地管理空间数据库模型设计与实现[J].地球信息科学学报,2010,12(3):348-358.
