Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks
Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks
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DOI：
10.1016/j.isprsjprs.2017.05.002
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发表时间：
2017-08-01
影响因子：
12.7
通讯作者：
Dalla Mura, Mauro
中科院分区：
文献类型：
--
作者：
Alshehhi, Rasha;Marpu, Prashanth Reddy;Dalla Mura, Mauro
从遥感图像中提取人造物体（例如道路和建筑物）在许多城市应用（例如城市土地利用和土地覆盖评估、更新地理数据库、变化检测等）中发挥着重要作用。这项任务通常很困难，因为数据复杂，外观异构，类内变化较大，类间变化较小。在这项工作中，我们提出了一种基于单一补丁的卷积神经网络（CNN）架构，用于从高分辨率遥感数据中提取道路和建筑物。在后处理阶段，将相邻区域的道路和建筑物的低级特征（例如，不对称性和紧凑性）与卷积神经网络（CNN）特征相结合，以提高性能。在两个具有挑战性的高分辨率图像数据集上进行了实验，以证明所提出的网络架构的性能，并将结果与其他基于补丁的网络架构进行比较。结果证明了所提出的网络架构在提取城市地区道路和建筑物方面的有效性和优越性能。 (C) 2017 年国际摄影测量与遥感协会 (ISPRS)。由 Elsevier B.V. 出版。保留所有权利。
Extraction of man-made objects (e.g., roads and buildings) from remotely sensed imagery plays an important role in many urban applications (e.g., urban land use and land cover assessment, updating geographical databases, change detection, etc). This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class and lower inter-class variations. In this work, we propose a single patch-based Convolutional Neural Network (CNN) architecture for extraction of roads and buildings from high-resolution remote sensing data. Low-level features of roads and buildings (e.g., asymmetry and compactness) of adjacent regions are integrated with Convolutional Neural Network (CNN) features during the post -processing stage to improve the performance. Experiments are conducted on two challenging datasets of high-resolution images to demonstrate the performance of the proposed network architecture and the results are compared with other patch-based network architectures. The results demonstrate the validity and superior performance of the proposed network architecture for extracting roads and buildings in urban areas. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.