论文标题
深度学习以识别卫星图像中的移动目标
Deep Learning for Recognizing Mobile Targets in Satellite Imagery
论文作者
论文摘要
对软件的需求不断增长，该软件自动检测并归类了卫星图像中的飞机，汽车和船舶等移动目标。这种自动化目标识别（ATR）软件的应用包括经济预测，交通计划，海上执法和灾难响应。本文介绍了卷积神经网络（CNN）的扩展，用于分类到滑动窗口算法以进行检测。它是对Xview数据集的移动目标进行评估的，该数据集的检测准确度高于95％。
There is an increasing demand for software that automatically detects and classifies mobile targets such as airplanes, cars, and ships in satellite imagery. Applications of such automated target recognition (ATR) software include economic forecasting, traffic planning, maritime law enforcement, and disaster response. This paper describes the extension of a convolutional neural network (CNN) for classification to a sliding window algorithm for detection. It is evaluated on mobile targets of the xView dataset, on which it achieves detection and classification accuracies higher than 95%.
