Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

中文翻译：

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用于城市土地利用分类的基于对象的卷积神经网络 (OCNN)

城市土地利用信息对于城市规划和区域管理等各种与城市相关的应用至关重要。因此，从非常精细的空间分辨率（VFSR）遥感图像中提取城市土地利用引起了遥感界的广泛关注。然而，从 VFSR 图像中对城市土地利用进行分类仍然是一项具有挑战性的任务，因为在区分复杂的空间模式以导出高级语义标签方面极其困难。深度卷积神经网络 (CNN) 具有提取高级空间特征的巨大潜力，这要归功于其具有多层次抽象的分层性质。然而，模糊的物体边界和几何失真，以及巨大的计算冗余，严重限制了CNN在城市土地利用分类中的潜在应用。在本文中，提出了一种新的基于对象的卷积神经网络 (OCNN)，用于使用 VFSR 图像进行城市土地利用分类。OCNN 不是逐像素卷积过程，而是依赖于分割对象作为其功能单元，并且 CNN 网络用于分析和标记对象，例如划分对象内和对象间的变化。开发了具有不同模型结构和窗口大小的两个 CNN 网络来预测线性形状的物体（例如公路、运河）和一般（其他非线性形状的）物体。然后执行基于规则的决策融合以整合特定于类的分类结果。所提出的 OCNN 方法的有效性在英国南安普敦和曼彻斯特的两个大型城市场景的航空摄影上进行了测试。OCNN 结合大小窗口大小实现了出色的分类精度和计算效率，始终优于其子模块以及其他基准比较器，包括像素级 CNN、基于上下文的 MRF 和基于对象的 OBIA-SVM 方法. 所提出的方法提供了第一个基于对象的 CNN 框架，可以有效地解决 VFSR 图像中城市土地利用分类的复杂问题。以及其他基准比较器，包括像素级 CNN、基于上下文的 MRF 和基于对象的 OBIA-SVM 方法。所提出的方法提供了第一个基于对象的 CNN 框架，可以有效地解决 VFSR 图像中城市土地利用分类的复杂问题。以及其他基准比较器，包括像素级 CNN、基于上下文的 MRF 和基于对象的 OBIA-SVM 方法。所提出的方法提供了第一个基于对象的 CNN 框架，可以有效地解决 VFSR 图像中城市土地利用分类的复杂问题。