For the object-based classification of high resolution remote sensing images, many people expect that introducing deep learning methods can improve then classification accuracy. Unfortunately, the input shape for deep neural networks (DNNs) is usually rectangular, whereas the shapes of the segments output by segmentation methods are usually according to the corresponding ground objects; this inconsistency can lead to confusion among different types of heterogeneous content when a DNN processes a segment. Currently, most object-based methods utilizing convolutional neural networks (CNNs) adopt additional models to overcome the detrimental influence of such heterogeneous content; however, these heterogeneity suppression mechanisms introduce additional complexity into the whole classification process, and these methods are usually unstable and difficult to use in real applications. To address the above problems, this paper proposes a simplified object-based deep neural network (SO-DNN) for very high resolution remote sensing image classification. In SO-DNN, a new segment category label inference method is introduced, in which a deep semantic segmentation neural network (DSSNN) is used as the classification model instead of a traditional CNN. Since the DSSNN can obtain a category label for each pixel in the input image patch, different types of content are not mixed together; therefore, SO-DNN does not require an additional heterogeneity suppression mechanism. Moreover, SO-DNN includes a sample information optimization method that allows the DSSNN model to be trained using only pixel-based training samples. Because only a single model is used and only a pixel-based training set is needed, the whole classification process of SO-DNN is relatively simple and direct. In experiments, we use very high-resolution aerial images from Vaihingen and Potsdam from the ISPRS WG II/4 dataset as test data and compare SO-DNN with 6 traditional methods: O-MLP, O+CNN, OHSF-CNN, 2-CNN, JDL and U-Net. Compared with the best-performing method among these traditional methods, the classification accuracy of SO-DNN is improved by up to 7.71% and 10.78% for single images from Vaihingen and Potsdam, respectively, and the average classification accuracy is improved by 2.46% and 2.91% for the Vaihingen and Potsdam images, respectively. SO-DNN relies on fewer models and easier-to-obtain samples than traditional methods, and its stable performance makes SO-DNN more valuable for practical applications.

对于高分辨率遥感图像的基于对象的分类，很多人期望引入深度学习方法可以提高分类精度。不幸的是，深度神经网络（DNN）的输入形状通常是矩形的，而分割方法输出的线段形状通常是根据相应的地面物体；当 DNN 处理一个片段时，这种不一致会导致不同类型的异构内容之间的混淆。目前，大多数利用卷积神经网络 (CNN) 的基于对象的方法都采用额外的模型来克服此类异构内容的不利影响；然而，这些异质性抑制机制给整个分类过程带来了额外的复杂性，并且这些方法通常不稳定，难以在实际应用中使用。为了解决上述问题，本文提出了一种简化的基于对象的深度神经网络（SO-DNN），用于超高分辨率遥感图像分类。在 SO-DNN 中，引入了一种新的段类别标签推理方法，其中使用深度语义分割神经网络（DSSNN）作为分类模型，而不是传统的 CNN。由于DSSNN可以为输入图像patch中的每个像素获得一个类别标签，不同类型的内容不会混在一起；因此，SO-DNN 不需要额外的异质性抑制机制。此外，SO-DNN 包括一种样本信息优化方法，该方法允许仅使用基于像素的训练样本来训练 DSSNN 模型。因为只使用单一模型，只需要一个基于像素的训练集，所以SO-DNN的整个分类过程比较简单直接。在实验中，我们使用来自 ISPRS WG II/4 数据集的 Vaihingen 和 Potsdam 的超高分辨率航拍图像作为测试数据，并将 SO-DNN 与 6 种传统方法进行比较：O-MLP、O+CNN、OHSF-CNN、2- CNN、JDL 和 U-Net。与这些传统方法中性能最好的方法相比，SO-DNN 对来自 Vaihingen 和 Potsdam 的单幅图像的分类准确率分别提高了 7.71% 和 10.78%，平均分类准确率提高了 2.46% 和Vaihingen 和 Potsdam 图像分别为 2.91%。SO-DNN 比传统方法依赖更少的模型和更容易获得的样本，