基于深度卷积网络的地理图像分类研究

最近五年，卷积神经网络（CNN）得到了充分的发展，在图像分类领域，基于监督学习的算法在相关任务中取得了巨大的成功。但是与分类极为准确地粗粒度标签数据集相比，细粒度标签数据集的分类依旧是一个难点。地理图像被广泛应用于社会的各个方面，研究者往往需要对大规模的地理图像数据进行分类，但是由于地理图像的特征差异较小，因此自动化分类是相对困难的。对地理图像的细粒度特征进行标记，通过深度卷积网络对其进行训练和学习，极大地提高地理图像的分类精度。

Research on Geographic Image Classification Based on Deep Convolution Network

In recent five years, convolutional neural network (CNN) has been fully developed. In the field of image classification, the algorithm based on supervised learning has achieved great success in related tasks. However, compared with coarse-grained label data sets, the classification of fine-grained label data sets is still a difficult point. Geographic images are widely used in all aspects of society. Researchers often need to classify large-scale geographic image data. However, due to the small feature difference of geographic images, automatic classification is relatively difficult. In this paper, the fine-grained features of geographic images are labeled, and trained and learned through the deep convolution network, which greatly improves the classification accuracy of geographic images.