【摘要】：針對目前無人機影像中單個建筑物角點的檢測現狀,提出了一種基于支持向量機(SVM)的無人機影像中建筑物的角點檢測方法。首先對4個波段的無人機影像進行多尺度分割,計算影像的NDVI,通過植被與非植被區(qū)域的波譜差異剔除植被的影響。其次,用面向對象分類法將"建筑物塊"從影像中提取出來,對"建筑物塊"區(qū)域用Harris算子進行邊緣檢測,形成建筑物邊緣點集數據。隨后通過設計高斯徑向基將邊緣樣本點映射到高維特征空間,構建特征向量,采用邊緣點集訓練SVM分類模型,最終通過SVM分類模型從粗提取的邊緣點集中檢測出正確的建筑物角點,實現了單個建筑物的角點提取。
[Abstract]:According to the current situation of single building corner detection in UAV image, a new method of building corner detection in UAV image based on support vector machine (SVM) is proposed. Firstly, the multi-scale segmentation of UAV images in four bands was carried out, the NDVI of the images was calculated, and the influence of vegetation was eliminated by the spectral differences between vegetation and non-vegetation regions. Secondly, the "building block" is extracted from the image by the object-oriented classification method, and the edge of the "building block" area is detected by Harris operator to form the building edge point set data. Then, the edge sample points are mapped to the high dimensional feature space by designing Gao Si radial basis function, and the feature vectors are constructed, and the classification model is trained by edge point set. Finally, the correct corner points of buildings are detected from rough edge points by SVM classification model, and the corner points of a single building are extracted.
【作者單位】： 桂林理工大學測繪地理信息學院;廣西空間信息與測繪重點實驗室;南寧市勘察測繪地理信息院;
【基金】：國家自然科學基金(41161073) 廣西自然科學基金(2016GXNSFAA380013;2014GXNSFDA118038) 桂林市科學研究與技術開發(fā)計劃(2016012601) 重慶基礎科學與前沿技術研究項目(cstc2015jcyj B028)
【分類號】：P237

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