基于特征优化的面向对象建筑物提取

Object-oriented Building Extraction Based on Feature Optimization

相比于基于像素的建筑物提取方法,面向对象方法能减少“异物同谱”和“同物异谱”现象,提高提取精度;针对遥感影像特征繁多,造成特征维数灾难的问题,本文提出了一种面向对象的特征优化方法进行建筑物提取.首先将最小误差自动阈值分割方法和多尺度分割相结合,优化分割技术;然后基于Relief算法和fast correlation-based filter (FCBF)算法进行特征选择,构建最优特征子集;最后使用随机森林方法进行建筑物提取并用最小外接矩形方法优化建筑物边界.结果显示,特征重要性差异较大,基于最优特征子集建筑物提取的总体精度达到0.93, Kappa系数为0.91,明显高于原始特征集和优化特征集提取结果.

Compared with pixel-based building extraction methods, object-oriented methods can reduce the phenomena of“the same spectrum for different objects” and “different spectra for the same object” and improve extraction accuracy. To address the curse of feature dimensionality due to numerous features of remote sensing images, this study proposes an object-oriented feature optimization method for building extraction. First of all, minimum error automatic threshold segmentation is combined with multi-scale segmentation to optimize the segmentation technology. Then, features are selected by the Relief algorithm and fast correlation-based filter(FCBF) algorithm to construct the optimal feature subset.Finally, buildings are extracted by the random forest method, and building boundaries are optimized by the minimum bounding rectangle method. The results show that the importance of features varies greatly. An overall accuracy of 0.93 is achieved by building extraction based on the optimal feature subset, and the Kappa coefficient is 0.91, which is significantly higher than the extraction results of the original feature set and the optimized feature set.