Abstract

Building recognition is a core task for urban image classification (mapping), especially in optical high-resolution imagery. Convolutional Neural Networks (CNNs) have recently achieved unprecedented performance in the automatic recognition of objects (e.g. buildings, roads, or trees) in high-resolution imagery. Although these results are promising, questions remain about generalizability. This is a great challenge, as there is a wide variability in the visual characteristics of the building image scene across different geographic locations. CNNs are overfitted with limited and low diversity samples and are tested on the same or nearby geographic locations. In this work, we propose two scenarios with regard to transfer learning CNN features for building scene classification. We also investigate the generalizability of CNNs for building recognition across different geographic locations. The results of the two scenarios show that the final model, generalizable in different geographic locations, unseen areas.

摘要

建筑物识别是城市图像分类（映射）的核心任务，尤其是在光学高分辨率图像中。卷积神经网络 (CNN) 最近在高分辨率图像中对象（例如建筑物、道路或树木）的自动识别方面取得了前所未有的性能。尽管这些结果很有希望，但关于普遍性的问题仍然存在。这是一个巨大的挑战，因为不同地理位置的建筑图像场景的视觉特征存在很大差异。CNN 过度拟合有限且多样性低的样本，并在相同或附近的地理位置进行测试。在这项工作中，我们提出了两种用于构建场景分类的迁移学习 CNN 特征的场景。我们还研究了 CNN 在不同地理位置建立识别的普遍性。两种情景的结果表明，最终模型可以在不同的地理位置推广，而且是看不见的区域。