论文标题

卫星图像分类与深度学习

Satellite Image Classification with Deep Learning

论文作者

Pritt, Mark, Chern, Gary

论文摘要

卫星图像对于包括灾难响应,执法和环境监测在内的许多应用非常重要。这些应用需要在图像中手动识别对象和设施。由于要涵盖的地理位置扩展非常好,并且可以进行搜索的分析师很少,因此需要自动化。然而,传统的对象检测和分类算法过于不准确,无法解决问题。深度学习是一种机器学习算法的家族,它表现出对此类任务自动化的希望。它通过卷积神经网络在图像理解中取得了成功。在本文中,我们将它们应用于高分辨率,多光谱卫星图像中的物体和设施识别问题。我们将深度学习系统描述为从IARPA功能图(FMOW)数据集分类为63个不同类别的对象和设施。该系统由卷积神经网络和其他神经网络组成,这些神经网络将卫星元数据与图像特征集成在一起。它是使用Keras和Tensorflow深度学习库在Python中实现的,并在Linux服务器上使用Nvidia Titan X图形卡运行。在撰写本文时,该系统在FMOW TopCoder竞赛中排名第二。它的总准确度为83%,F1分数为0.797,其精度为95%或更高的类别为15个类别。

Satellite imagery is important for many applications including disaster response, law enforcement, and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic expanses to be covered are great and the analysts available to conduct the searches are few, automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that have shown promise for the automation of such tasks. It has achieved success in image understanding by means of convolutional neural networks. In this paper we apply them to the problem of object and facility recognition in high-resolution, multi-spectral satellite imagery. We describe a deep learning system for classifying objects and facilities from the IARPA Functional Map of the World (fMoW) dataset into 63 different classes. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. It is implemented in Python using the Keras and TensorFlow deep learning libraries and runs on a Linux server with an NVIDIA Titan X graphics card. At the time of writing the system is in 2nd place in the fMoW TopCoder competition. Its total accuracy is 83%, the F1 score is 0.797, and it classifies 15 of the classes with accuracies of 95% or better.

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