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首页> 外文期刊>Remote Sensing >A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China
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A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China

机译：一种深度卷积神经网络的土地覆盖制图方法-以秦皇岛市为例

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摘要

Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.
机译：土地覆盖及其动态信息是表征地表条件，支持土地资源管理和优化以及评估气候变化和人类活动影响的基础。在土地覆盖信息提取中，传统的卷积神经网络（CNN）方法存在一些问题，例如无法应用于多光谱和高光谱卫星图像，模型的泛化能力弱以及难以自动构建训练结构数据库。为了解决这些问题，本研究提出了一种基于Landsat-8作战陆地成像仪（OLI）图像的新型深度卷积神经网络。该网络将级联的跨通道参数池和平均池层集成在一起，应用分层采样策略来实现训练数据集的自动构建，确定模型相关参数的技术方案，最后进行遥感图像的自动分类。本研究使用新型的深度卷积神经网络从河北秦皇岛市提取土地覆盖信息，并将实验结果与传统方法进行了比较。结果表明：（1）提出的深度卷积神经网络模型可以自动构造训练数据集并对图像进行分类。该模型使用深度神经网络对多光谱和高光谱卫星图像进行分类，从而提高了模型的泛化能力，简化了模型的应用。 （2）提出的DCNN模型在秦皇岛地区提供了最好的分类结果。获得的土地覆盖数据的总体准确度为82.0％，卡伯系数为0.76。与支持向量机方法和最大似然分类方法相比，总体准确性分别提高了5％和14％。

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