Object-oriented Classification of High Spatial Resolution Remote Sensing Imagery Based on Image Segmentation with Pixel Shape Feature

According to the high resolution and rich spatial information of high spatial resolution remote sensing imagery,this paper proposes to integrate geometric,shape,texture feature for high spatial resolution remote sensing imagery classification in urban area with object-oriented method.The proposed method is a four-step classification routine that involves the integration of:①extraction of geometric shape feature;②segmentation of high spatial resolution remote sensing imagery based on spectrum information and geometric shape feature that extracted;③extraction of object shape feature,texture feature,spectal feature and so on,then use mutual information minimum redundancy and maximum relevance(mRMR)criterion to select optimal subset features;④support vector machine(SVM) for classification.To validate the proposed method,a case study with IKONOS high spatial resolution remote sensing imagery in Fuzhou city is implempented.The experimental results demonstrate that fused pixel shape index(PSI)feature can improve the multiscale segmentation sigificantly,and feature selection can acquire optial feature subset.Moreover,the proposed method for high spatial resolution remote sensing imagery classification in urban area can increase classification accuracy by about 6% in terms of overall accuracy compared with the nearest neighborhood method.