ABSTRACT

The ground breaking performance of fully convolutional neural nets (FCNs) for semantic segmentation tasks has yet to be achieved for landcover classification, partly because a lack of suitable training data. Here the FCN8 model is trained and evaluated in real-world conditions, so called in-the-wild, for the classification of opium poppy and cereal crops at very high resolution (1 m). Densely labelled image samples from 74 Ikonos scenes were taken from 3 years of opium cultivation surveys for Helmand Province, Afghanistan. Models were trained using 1 km² samples, sub-sampled patches and transfer learning. Overall accuracy was 88% for a FCN8 model transfer-trained on all 3 years of data and complex features were successfully grouped into distinct field parcels from the training data alone. FCNs can be trained end-to-end using variable sized input images for pixel-level classification that combines the spatial and spectral properties of target objects in a single operation. Transfer learning improves classifier performance and can be used to share information between FCNs, demonstrating their potential to significantly improve land cover classification more generally.

摘要

用于土地分割的全卷积神经网络（FCN）的语义分割任务具有突破性的性能，部分原因是缺乏合适的训练数据。在这里，FCN8模型是在真实世界的条件下（即所谓的野外条件）进行训练和评估的，用于以非常高分辨率（1 m）对罂粟和谷物作物进行分类。来自伊拉克赫尔曼德省3年的鸦片种植调查的74个Ikonos场景中带有密集标签的图像样本。使用1 km ²训练模型样本，子样本补丁和迁移学习。FCN8模型在所有3年的数据上进行了转移训练，总体准确性为88％，仅从训练数据中就已将复杂特征成功地分为不同的区域。可以使用可变大小的输入图像进行端到端的FCN训练，以进行像素级分类，在单个操作中结合目标对象的空间和光谱特性。转移学习提高了分类器的性能，可用于在FCN之间共享信息，证明了它们有潜力更广泛地显着改善土地覆被分类。