论文标题
深度神经网络，用于自动提取时间序列卫星图像中的特征
Deep Neural Networks for automatic extraction of features in time series satellite images
论文作者
论文摘要
许多地球观察计划，例如Landsat，Sentinel，Spot和Pleiades，每天都会在时间序列中组织大量的中至高分辨率多光谱图像。在这项工作中，我们利用这些图像提供的时间和空间信息来生成覆盖地图。为此，我们将完全卷积的神经网络与卷积长的短期记忆相结合。提供了拟议的时空神经网络架构的实施细节。实验结果表明，时间序列图像提供的时间信息允许提高土地覆盖分类的准确性，从而产生最新的地图，可以帮助识别地球上的变化。
Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. In this work, we exploit both temporal and spatial information provided by these images to generate land cover maps. For this purpose, we combine a fully convolutional neural network with a convolutional long short-term memory. Implementation details of the proposed spatio-temporal neural network architecture are provided. Experimental results show that the temporal information provided by time series images allows increasing the accuracy of land cover classification, thus producing up-to-date maps that can help in identifying changes on earth.
