How to reduce the difference between segmentation result and practical geographical object is a difficult problem faced by highresolution remote sensing image segmentation currently. Aiming at this issue, in this paper a new OC (Object Confidence) index is constructed to measure the matching degree between any region and geographical object, and a multiscale segmentation algorithm facing to geographical objects is proposed. This algorithm mainly contains two steps: firstly, this algorithm establishes an initial seed regional set through conducting over segmentation to the image and determines the scale parameter set; secondly, this algorithm guides the process of multiscale region merging through tracking the interscale change of OC index, and makes the region merging result gradually approach to practical geographical object. The multigroup experiments indicate that the proposed algorithm can obviously improve the oversegmentation and insufficientsegmentation problems, and identify the complete outlines of buildings, roads as well as other geographical objects accurately. The proposed algorithm is obviously superior to the commercial software eCongnition and traditional multiscale segmentation algorithm in both qualitative analysis and quantitative precision evaluation.