Accurate land-use classification is essential for the management and supervision of urban development, land resources and environment sustainability. Feature extractor and classifier are the important modules of land-use classification. Deep convolutional neural network has been proved to be able to learn more robust and discriminative features from images. In this paper, we increase the diversity and discriminative of features by fusing features extracted by three deep convolutional neural networks with different architectures, which are obtained by fine-tuning the pre-trained models with land-use image dataset. In order to make the classification faster and have excellent generalization performance, we select constrained extreme learning machine instead of fully connected layer or support vector machine. Experimental results show that the proposed method can achieve a better performance with the overall classification accuracy of 98.35%, compared with other state-of-the-art methods.

准确的土地利用分类对于城市发展，土地资源和环境可持续性的管理和监督至关重要。特征提取器和分类器是土地利用分类的重要模块。深度卷积神经网络已被证明能够从图像中学习更强大和更具区分性的特征。在本文中，我们通过融合三个具有不同架构的深层卷积神经网络提取的特征来增加特征的多样性和判别力，这些特征是通过用土地利用图像数据集对预训练模型进行微调而获得的。为了使分类更快并具有出色的泛化性能，我们选择了约束极限学习机来代替全连接层或支持向量机。