基于深度学习的卫星图像识别分类方法

卫星遥感技术是一种非常重要的地球空间监测技术.卫星遥感图像经过处理后具有数据量大和数据类型复杂多样的特点，传统方法进行识别分类耗费大量人力物力.为了降低工作量，并为后续处理提供便利，本文将深度学习算法应用于卫星图像的识别分类中，设计了一种基于VGGNet的识别分类方法，利用除雾算法对训练数据进行数据增强处理，并添加岭回归正则化层，利用标签之间的相关性进行预测，使得方法达到90%以上的F2 score，并在实验部分进行了对比验证.最后利用此方法搭建了一个基于Django的在线识别分类展示系统.

Satellite Image Recognition and Classification Method Based on Deep Learning

Satellite remote sensing technology is a very important geo-spatial monitoring technology. After being processed, the satellite remote sensing images have a large amount of data characteristics of various complex data types, the traditional target classification and recognition ways spend a lot of manpower and material resources. In order to reduce the workload and provide convenience for subsequent processing, we consider using deep learning algorithms for satellite images classification and recognition. In this paper, we designed an image recognition and classification method based on VGGNet. We augmentated data by using haze removal algorithm and other tricks. And we added ridge regression to use correlations between labels to predict. Verified by experiment comparison, this method can achieve more than 90% of F2 score. Finally, an online recognition, classification and display system based on Django is built by using this method.