The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural image analysis, their application to multi-spectral satellite images (wherein input images have a large number of channels) remains relatively unexplored. In this paper, we compare different methods of leveraging multi-band information with CNNs, demonstrating the performance of all compared methods on the task of semantic segmentation of agricultural vegetation (vineyards). We show that standard industry practice of using bands selected by a domain expert leads to a significantly worse test accuracy than the other methods compared. Specifically, we compare: using bands specified by an expert; using all available bands; learning attention maps over the input bands; and leveraging Bayesian optimisation to dictate band choice. We show that simply using all available band information already increases test time performance, and show that the Bayesian optimisation, first applied to band selection in this work, can be used to further boost accuracy.

中文翻译：

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卷积神经网络对多光谱卫星数据的优化利用

卫星图像分析将被证明是追求可持续发展的关键工具。尽管卷积神经网络（CNN）在自然图像分析中取得了很大的进步，但是它们在多光谱卫星图像（其中输入图像具有大量通道）中的应用仍然相对未开发。在本文中，我们比较了利用多波段信息与CNN的不同方法，论证了所有比较方法在农业植被（葡萄园）语义分割任务中的性能。我们表明，使用行业专家选择的频段的标准行业惯例比其他方法导致的测试准确性明显差。具体来说，我们比较：使用专家指定的频段；使用所有可用频段；学习输入波段上的注意力图；并利用贝叶斯优化来决定频段选择。我们证明，简单地使用所有可用的波段信息已经可以提高测试时间性能，并且表明在这项工作中首先应用于波段选择的贝叶斯优化可以进一步提高准确性。