Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km². Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (“medial axis”) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN₁₆ or CNN₂₅₆, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution.

卷积神经网络（CNN）已越来越多地用于遥感影像的土地覆盖制图。但是，使用传统CNN进行大面积分类的计算量很大，并且使用滑动窗口方法会生成粗糙的地图。为了解决此问题，基于对象的CNN（OCNN）成为提高分类性能的替代解决方案。但是，以前的研究主要集中在城市区域或小场景，对于异质景观的大面积分类，仍然需要实现OCNN方法。此外，对分段对象进行大规模标记需要一种实用的方法，以减少计算量，包括对象分析和多个CNN。这项研究提出了一种新的多尺度OCNN（multi-OCNN）框架，用于在145,740 km上以1-m分辨率进行大规模土地覆盖分类²。我们的方法包括三个主要步骤：（i）图像分割，（ii）使用基于骨架的算法进行对象分析以及（iii）应用多个CNN进行最终分类。此外，我们开发了一个大型基准数据集，称为IowaNet，其中包含100万个带标签的图像和10个类。在我们的方法中，对多尺度CNN进行了训练，以在对象的语义标记过程中捕获最佳上下文信息。同时，骨架化算法提供了对象的形态表示（“中间轴”），以支持选择卷积位置进行CNN预测。总体而言，与传统的基于补丁的CNN（81.6％）和固定输入OCNN（82％）相比，拟议的多OCNN表现出更好的分类准确性（总体准确性〜87.2％）。另外，结果表明此框架是8.1和111。₁₆或CNN ₂₅₆。事实证明，多个CNN和对象分析对于准确和快速的分类至关重要。虽然多OCNN在土地覆盖产品中产生了高水平的空间细节，但在某些类别中却观察到了错误分类，例如道路与建筑物或阴影与湖泊。尽管存在这些小缺陷，我们的结果也证明了IowaNet训练数据集在模型性能方面的好处；随着样本数量的增加，过拟合过程减少。多重OCNN的局限性部分由航拍数据中的分割质量和有限的频谱带数量来解释。随着深度学习方法的发展，这项研究支持了以1-m分辨率运行的大规模土地覆被产品的多种OCNN好处。