论文标题
使用FCN-8对RGB卫星图像进行LULC分割
LULC Segmentation of RGB Satellite Image Using FCN-8
论文作者
论文摘要
这项工作介绍了将完全卷积网络（FCN-8）用于将高分辨率RGB地面卫星卫星图像的语义分割成土地使用土地覆盖（LULC）类别。具体而言，我们提出了一种非重叠的网格方法，以培训具有VGG-16重量的完全符号网络（FCN-8），以将卫星IMAGE分为四个（森林，建筑，农田和水）。 FCN-8在语义上将编码器较低分辨率中的区分特征投射到更高分辨率的像素空间上，以获得密集的分类。我们使用GaOfen-2图像数据集尝试了提出的系统，其中包含150张中国60多个城市的图像。为了进行比较，我们使用了可用的地面真实性以及使用广泛使用的Commereriial GIS软件进行分割的图像。通过提出的非重叠网格方法，FCN-8比Ecognition软件软件获得了明显提高的性能。我们的模型达到了91.0％的平均准确性，平均相互交换为0.84。相比之下，生态认知的平均准确性为74.0％，而IOU为0.60。本文还报告了在LULC边界发生错误的详细分析。
This work presents use of Fully Convolutional Network (FCN-8) for semantic segmentation of high-resolution RGB earth surface satel-lite images into land use land cover (LULC) categories. Specically, we propose a non-overlapping grid-based approach to train a Fully Convo-lutional Network (FCN-8) with vgg-16 weights to segment satellite im-ages into four (forest, built-up, farmland and water) classes. The FCN-8 semantically projects the discriminating features in lower resolution learned by the encoder onto the pixel space in higher resolution to get a dense classi cation. We experimented the proposed system with Gaofen-2 image dataset, that contains 150 images of over 60 di erent cities in china. For comparison, we used available ground-truth along with images segmented using a widely used commeriial GIS software called eCogni-tion. With the proposed non-overlapping grid-based approach, FCN-8 obtains signi cantly improved performance, than the eCognition soft-ware. Our model achieves average accuracy of 91.0% and average Inter-section over Union (IoU) of 0.84. In contrast, eCognitions average accu-racy is 74.0% and IoU is 0.60. This paper also reports a detail analysis of errors occurred at the LULC boundary.
