针对高分辨率遥感图像边缘突出、 上下文信息丰富等特点， 提出一种融合边缘特征的区域分割算法， 基于面向对象图像分析方法， 综合考虑遥感图像的光谱和空间特征。 首先应用SUSAN算子对全色波段图像提取边缘信息， 然后对融合后的彩色图像进行两阶段分割， 第一阶段采用倒四叉树融合成初始图像对象， 在第二阶段中通过在区域异质性判据中增加边界强度特征的方式融合已提取的边缘信息进行分级区域合并， 形成图像分割结果。 文中用三峡库区某区域QuickBird数据进行了实验， 并与ENVI Zoom和Definiens下的分割结果进行了效果对比和定量评价， 结果表明该方法可行、 有效

Satellite sensor technology endorsed better discrimination of various landscape objects. Image segmentation approaches to extracting conceptual objects and patterns hence have been explored and a wide variety of such algorithms abound. To this end, in order to effectively utilize edge and topological information in high resolution remote sensing imagery, an object-oriented algorithm combining edge detection and region merging is proposed. Susan edge filter is firstly applied to the panchromatic band of Quickbird imagery with spatial resolution of 0.61 m to obtain the edge map. Thanks to the resulting edge map, a two-phrase region-based segmentation method operates on the fusion image from panchromatic and multispectral Quickbird images to get the final partition result. In the first phase, a quad tree grid consisting of squares with sides parallel to the image left and top borders agglomerates the square subsets recursively where the uniform measure is satisfied to derive image object primitives. Before the merger of the second phrase, the contextual and spatial information, (e.g., neighbor relationship, boundary coding) of the resulting squares are retrieved efficiently by means of the quad tree structure. Then a region merging operation is performed with those primitives, during which the criterion for region merging integrates edge map and region-based features. This approach has been tested on the QuickBird images of some site in Sanxia area and the result is compared with those of ENVI Zoom and Definiens. In addition, quantitative evaluation of the quality of segmentation results is also presented. Experiment results demonstrate stable convergence and efficiency.