ABSTRACT

Landsat and Sentinel-2 are two freely accessible satellite data that are relevant for global land cover monitoring. However, the uses of the latter data set are growing because of its higher spatial resolutions and the availability of benchmark data sets for deep learning applications. In this study, we integrate a style transfer (perceptual loss estimation from Sentinel 2 benchmark data) into a Generative Adversarial Network (GAN) to construct a single image super-resolution model. The proposed model upscales Landsat 8 images (using red, green, blue, and near-infrared bands at 30 m and Panchromatic band 15 m for high-resolution features exploiting) to 10 m (with Sentinel-2 as reference). Compared to pan-sharpening and other upscaling methods, the proposed method can produce more realistic, spatial convincing images at 10 m resolution and more similar to Sentinel-2 images than the other commonly used super-resolution imaging algorithms. As a result, the proposed method extends the usage of high-resolution benchmark data sets for lower resolution imagery to enrich supplement data sources for land cover classification.

摘要

Landsat和Sentinel-2是两个可免费获取的卫星数据，与全球土地覆盖监测相关。但是，由于后者的更高的空间分辨率以及基准数据集在深度学习应用中的可用性，后一种数据集的使用正在增长。在这项研究中，我们将样式转换（来自Sentinel 2基准数据的感知损失估计）集成到生成对抗网络（GAN）中，以构建单个图像超分辨率模型。拟议的模型将Landsat 8图像（使用30 m的红色，绿色，蓝色和近红外波段和15 m的全色波段用于高分辨率特征开发）放大到10 m（以Sentinel-2为参考）。与泛锐化和其他放大方法相比，该方法可以产生更逼真的效果，10 m分辨率的空间说服力图像，比其他常用的超分辨率成像算法更类似于Sentinel-2图像。结果，所提出的方法扩展了高分辨率基准数据集用于低分辨率图像的用途，以丰富用于土地覆盖分类的补充数据源。