利用词袋模型检测建筑物顶面损毁区域

针对航空影像中已分割出的建筑物顶面，提出了一种利用视觉词袋模型检测建筑物顶面损毁区域的方法。该方法首先利用简单线性迭代聚类方法对建筑物顶面进行超像素分割，然后对超像素区域利用颜色和梯度方向直方图特征构建视觉词袋模型，最后使用支持向量机（support vector machine，SVM）对超像素区域中的损毁区域进行检测。实验结果表明，该方法能有效判定建筑物顶面损毁区域，对提高建筑物整体损毁检测精度具有重要意义。

Detection of Damaged Areas Based on Visual Bag-of-Words Model from Aerial Remote Sensing Images

An approach for damaged rooftops areas detection is proposed based on visual bag-of-words model. First, the building rooftop is segmented into different superpixel areas using simple linear iterative clustering(SLIC) method, then features of color and histograms of oriented gradients are extracted from each superpixel area and the visual bag-of-words (BoW) model is employed to build the semantic feature vectors of damaged and non-damaged area. Finally, damaged and non-damaged parts of rooftop superpixel areas are discriminated using SVM. Experimental results show that the proposed method can be feasible and effective for detection of damaged rooftop areas, which is an important significance for improving the accuracy of overall building damaged detection.