ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-4
https://doi.org/10.5194/isprs-annals-IV-4-187-2018
https://doi.org/10.5194/isprs-annals-IV-4-187-2018
19 Sep 2018
 | 19 Sep 2018

BOOTSTRAPPED CNNS FOR BUILDING SEGMENTATION ON RGB-D AERIAL IMAGERY

C. Sebastian, B. Boom, T. van Lankveld, E. Bondarev, and P. H. N. De With

Keywords: bootstrapping, deep learning, building segmentation, aerial imagery

Abstract. 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.