面向人类视觉感知的高分辨率遥感图像检索研究

项目名称： 面向人类视觉感知的高分辨率遥感图像检索研究

项目编号： No.41201453

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 地理学

项目作者： 朱先强

作者单位： 中国人民解放军国防科学技术大学

项目金额： 24万元

中文摘要： 空间探测技术的飞速发展使得海量高分辨率遥感数据的获取成为可能，应用实践中却仍然面临着信息匮乏的窘境，关键原因在于数据处理能力滞后，特别是缺乏有效的检索手段，使得大量有效信息淹没在数据海洋中。本课题以从复杂高分辨率遥感数据数据中提取出符合视觉感知的感兴趣场景为目标，研究视觉空间中多通道特征的语义表达机制、包含个性信息的视觉感知模型、任务驱动的复杂场景检索算法等内容。首先针对高分辨率卫星数据特征空间维度高、覆盖类型复杂的特点，分析局部特征的视觉刺激能力和辅助解译能力，研究能够表征综合语义信息的互补性视觉特征集合；在此基础上，引入注视焦点、地理对象等个性化概念信息，提出包含像素、基元及概念的层次化视觉感知模型以建立图像、特征及概念知识之间的映射；最后研究视觉期望引导下，研究复杂场景的视觉特征空间建模及视觉相似性度量，缩小视觉感知模型与实际视觉注意方式的差距，提出更为逼真的面向人类视觉感知的检索方

中文关键词： 高分辨率卫星图像；局部显著特征；图像检索；对象显著性；注意焦点

英文摘要： With rapid development of space exploriation technologies， the acquisition of massive high resolution remote sensing data comes true, while during the application we still face to effective information deprivation. The key problem lies in the limited data processing and analysis capability, especially lack of effective retrieval means, so that vast quantities of information are submerged in the ocean of data. This project takes aim at extracting interesting scene which should be according with the characteristics of human visual perception from complex high resolution satellite data, main research content includes semantic representation mechanism based on multi-channel visual features, visual perception model with personalized information, task-driven complex scene retrieval algorithm and so on. As high resolution data is charateristiced with high dimension and diverse land cover, we firstly analysis the visual stimulation ability and assitant interpretation capacity of local visual features, then give a complementarity feature set to describe the image comprehensive semantic. with this basis, personalized concepts such as attention focus, objective spatial object are imported to hierarchical visual attention model which contains three layers: pixel, element and concept, with assitant of this model the mappin

英文关键词： High resolution satellite image；local saliency feature；imagery retrieval；object saliency；visual attention key point