Automatic Geographic Information Classification Method Based on SVM for Site Selection of Stretch Field

Abstract

In tension stringing construction, the site selection of the stretch field is related to the efficiency, safety and cost of the whole wire construction. Surrounding geographic information impacts the site selection extremely seriously. Accurate grasp of the surrounding geographic information along the transmission line is the key to ensure the optimal site selection of the stretch field. Aiming at the geographic image data along the transmission lines collected by the UAV, firstly, the map data is blocked using the image gradient information and the geometric topological relationship in the image; then, the histogram features of the gradient direction are extracted from the blocked data; finally, the support vector machine is used to train the extracted features, and the map data is classified according to the model obtained from the training, and finally the automatic classification and recognition of the geographic information along the transmission lines is obtained. The proposed method can achieve high recognition accuracy by using small samples for training. Through practice, the proposed method can accurately classify map data, thus providing a reliable guarantee for determining the surrounding geographic influences when selecting a site for a stretch field.