Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet.First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.

本文介绍了一种用于提高机器视觉中自动化建筑识别性能的方法，该方法结合了卷积神经网络和Bootstrapping技术，只利用原训练数据的六分之一，并在一个航空图像数据集上获得了高达95.10％的精度-召回率平衡。

基于RGB-D航空图像的自举卷积神经网络建筑物分割