【作者】 刘建华；

【导师】 毛政元；

【作者基本信息】 福州大学 ， 通信与信息系统， 2011， 博士

【摘要】 随着遥感对地观测技术的不断进步，遥感影像的时、空、谱分辨率越来越高，影像数据量爆炸与处理能力严重滞后的矛盾日益尖锐，传统的影像处理技术面临新的挑战，发展更加高效的遥感影像数据挖掘理论与技术方法成为应对这一挑战的一种趋势。面向对象的遥感影像分析正是在此过程中逐渐形成的一个研究领域，影像分割是其中的一项关键技术，也是面向对象遥感影像分析、理解与应用中的难点。现有文献中介绍的遥感影像分割方法以及集成在商用遥感影像处理软件平台中的相关算法均存在不同程度的局限性，本文针对现有研究工作中的不足，探索高空间分辨率遥感影像的自适应分割方法，主要研究内容与成果如下：（1）归纳总结遥感影像分割研究领域现有的代表性研究成果（重点介绍现有主流遥感影像分割方法分类谱系中的各种方法），指出该领域目前存在的主要问题（分割尺度参数模型的适应性差、多尺度分割中对象特征信息的利用与耦合不足、分割算法缺乏自适应性、多尺度分割对象与语义影像目标不一致）与未来的发展趋势。（2）研究高空间分辨率遥感影像预处理步骤中融合和滤波技术与分割效果的关系，以QuickBird影像为样本数据检验通过影像融合实现高空间分辨率遥感影像全色波段与多光谱波段优势互补、通过影像滤波抑制语义影像目标内部光谱异质性与同时保持边缘信息的可行性，探索基于分割效果评价与选择融合和滤波处理方法的新思路。（3）基于输出融合策略与Canny算子，提出适用于高空间分辨率遥感影像的矢量、加权矢量与标量边缘检测新算法，并利用该算法在多色彩空间中实现了高空间分辨率遥感影像边缘信息的有效提取，同时研究不同地物类型对边缘检测响应程度的差异以及该差异对边缘提取的影响。（4）面向高空间分辨率遥感影像数据多色彩空间分割的特点，对均值漂移算法进行改进，实现了多色彩空间空-值域联合的多尺度均值漂移算法，分析比较该算法在不同色彩空间中分割高空间分辨率遥感影像的效果，验证了RGB与IHS色彩空间所具有的相对优势。（5）基于嵌入式集成策略，提出并实现了集成边缘与区域信息、空-值域联合的自适应多尺度均值漂移算法（Adaptive Integration of Canny and Mean Shift Segmentationalgorithm，AICMS）。分别以GeoEye、QuickBird以及航空影像三种高空间分辨率遥感影像为样本数据验证该算法对高空间分辨率影像数据类型的普适性及其在自适应性方面相对于商业软件eCognition中所集成的多尺度分割算法的优势。更多还原

【Abstract】 With the rapid development of remote sensing technology, temporal, spatial and spectralresolutions of Remotely Sensed Imagery (RSI) are higher and higher. The contradictionbetween image data explosion and serious lag in capabilities for dealing with them isincreasingly acute, which challenges traditional image processing techniques. Developing moreefficient theory and technical methods for mining RSI data becomes a tendency to address thechallenge, during which a promising research field, object-oriented RSI analysis was graduallyformed. Image segmentation is one of the key technologies in object-oriented RSI analysis,which is also the hardship of object-oriented image analysis, understanding and application. Sofar, the achieved Remotely Sensed Imagery Segmentation Methods (RSISM) still have variouslimitations and defects. In view of this, the thesis explores adaptive segmentation methods forHigh Spatial Resolution Remotely Sensed Imagery (HSRRSI). Its main research contents andrelated achievements are as follows:(1) Presently available representative achievements in the research field of RSIsegmentation were reviewed and summarized (focusing on various RSISM in mainstreamcategory pedigree), the main current problems (including the poor adaptability of segmentationscale parameter model, the short of utilizing and coupling of object feature information inmulti-scale segmentation, the lack of self-adaptability of algorithms, the inconsistent betweenimage objects by multi-scale segmentation and their corresponding semantic image objects) andpossible researching trend of which were also put forward.(2) The relationship of segmentation effect and fusion in conjunction with filter technologyin HSRRSI preprocessing steps was studied. By taking QuickBird image as sample data in casestudy, respectively verified was the feasibility of integrating advantages of panchromatic andmulti-spectral data of HSRRSI by fusing, and smoothing spectral heterogeneity within semanticimage objects and simultaneously keeping its edge information by filtering. A new idea, whichevaluates and selects image fusion and filter methods based on segmentation effect, has alsobeen explored.(3) By means of output fusion strategy and Canny operator, it proposed new edge detectionalgorithms respectively based on vector, and weighted vector or scalar, which were suitable forHSRRSI. In the multi-colorspace, the algorithms were employed to effectively extract edgeinformation from HSRRSI. Also presented were the differences of spectral responsiveness among various kinds of land cover types and their impacts on edge extraction.(4) Under considering characteristics of segmentation of HSRRSI data in multi-colorspace,the mean shift algorithm was improved, based on which the multi-colorspace,spatial-range-union, and multi-scale mean shift algorithm was implemented. The segmentationresults derived from the algorithm in different colorspace were analyzed and compared witheach other, and which verified the relative advantages of RGB and IHS colorspace.(5) Based on embedded integration strategy, a segmentation algorithm, named AdaptiveIntegration of Canny and Mean Shift Segmentation algorithm (AICMS), of adaptive multi-scale,spatial-range-union, and integrating edge and region was proposed and implemented. Byrespectively taking GeoEye, QuickBird and aerial photography images as sample data, theuniversality was proved. Also proved was the relative advantages of AICMS algorithm inadaptive property superior to the multi-scale segmentation algorithm embedded in eCognition(a commercialized software platform).更多还原