中文摘要：

随着遥感图像数量的急剧增加,如何进行高效检索已成为遥感图像信息提取和共享的瓶颈问题,基于内容的遥感图像检索因此逐渐成为了研究热点。本文提出了一种新的遥感图像检索方法,该方法综合利用了图像的色调和纹理特征。其基本过程是：首先,对图像进行主成分变换,对变化后的第一主成分图像进行五叉树分解,将大幅面的遥感图像分成一系列的子图像;然后,利用多通道Gabor滤波器与子图像做卷积运算,提取其纹理特征,同时计算像元值的方差和三阶矩作为各子图像的色调特征;最后,以子图像为特征基元,构建图像的色调直方图和纹理直方图,以多特征直方图匹配方法计算图像相似度实现遥感图像检索。利用高分辨率遥感影像的检索实验证明该方法是有效的。

英文摘要：

Nowadays,vast amount of remote sensing data have been acquired with the rapid development of Earth Observation System（EOS）.It has become a serious task to manage and use these data for most RS and GIS applications.The content-based retrieval system for remote sensing images（CBRSIR） has become resultingly a hot research field with the potential to retrieval interesting information from image databases automatically and intelligently.In this work,we put forward a new remote sensing image retrieval approach by using multi-features including image color and texture.Firstly,a given image is processed by principal components analysis and then decomposed by Quin-tree,which splits large-scale remote sensing imagery into sub images.Secondly,texture features of each image block are extracted via multi-channel Gabor filters,and the standard deviation and third moment of each sub image are extracted as color features.Then,color and texture histograms are constructed based on sub images.Finally,we compare the similarity of the color and texture histograms between the query example and each one in the image database.If the total similarity is higher than some threshold,the image will be returned.These images are sorted according their similarity as the final retrieval results.This approach is validated using high resolution remote sensing images.