论文标题
使用FastFCN对卫星图像的语义分割进行LULC分类
LULC classification by semantic segmentation of satellite images using FastFCN
论文作者
论文摘要
本文分析了快速完全卷积网络（FASTFCN）在语义上段的卫星图像，从而对土地使用/土地覆盖（LULC）类别进行了分类。 Fast-FCN在Gaofen-2图像数据集（GID-2）上使用，以五个不同的类别进行分割：建筑，草地，农田，水和森林。结果表明，与使用FCN-8或Ecognition（一种易于使用的软件）相比，与联合（MIOU）（MIOU）（MIOU）（MIOU）（MIOU）（MIOU）（MIOU）（0.97）（0.97）（0.97）（0.97）的准确性（0.93），召回（0.98）（0.98）（0.98）（0.98）。我们提出了结果之间的比较。我们建议FASTFCN比其他现有的LULC分类方法更快，更准确。
This paper analyses how well a Fast Fully Convolutional Network (FastFCN) semantically segments satellite images and thus classifies Land Use/Land Cover(LULC) classes. Fast-FCN was used on Gaofen-2 Image Dataset (GID-2) to segment them in five different classes: BuiltUp, Meadow, Farmland, Water and Forest. The results showed better accuracy (0.93), precision (0.99), recall (0.98) and mean Intersection over Union (mIoU)(0.97) than other approaches like using FCN-8 or eCognition, a readily available software. We presented a comparison between the results. We propose FastFCN to be both faster and more accurate automated method than other existing methods for LULC classification.
