SEN2NAIP: Sentinel-2图像超分辨率大规模数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Cesar Aybar, David Montero, Julio Contreras, Simon Donike, Freddie Kalaitzis, Luis Gómez-Chova
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引用次数: 0

摘要

随着遥感领域对高空间分辨率的需求不断增长,超分辨率(SR)算法的需求日益突出,该算法可以将低分辨率(LR)图像提升到高分辨率(HR)图像。为了解决这个问题,我们提出了SEN2NAIP,这是一个新颖而广泛的数据集,专门用于支持SR模型训练。SEN2NAIP包括两个主要组件。第一个是一组2,851对LR-HR图像,每个图像覆盖1.46平方公里。这些对是利用Sentinel-2 (S2)的LR图像和国家农业图像计划(NAIP)相应的HR图像生成的。利用这个跨传感器数据集,我们开发了一个退化模型,能够将NAIP图像转换为匹配S2图像的特征(S2 1 i k e)。这导致创建第二个子集,由35,314个NAIP图像及其相应的s2 i k k对应图像组成,使用退化模型生成。利用SEN2NAIP数据集,我们的目标是提供一个有价值的资源,促进探索提高Sentinel-2图像空间分辨率的新技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution.

The increasing demand for high spatial resolution in remote sensing has underscored the need for super-resolution (SR) algorithms that can upscale low-resolution (LR) images to high-resolution (HR) ones. To address this, we present SEN2NAIP, a novel and extensive dataset explicitly developed to support SR model training. SEN2NAIP comprises two main components. The first is a set of 2,851 LR-HR image pairs, each covering 1.46 square kilometers. These pairs are produced using LR images from Sentinel-2 (S2) and corresponding HR images from the National Agriculture Imagery Program (NAIP). Using this cross-sensor dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of S2 imagery ( S 2 l i k e ). This led to the creation of a second subset, consisting of 35,314 NAIP images and their corresponding S 2 l i k e counterparts, generated using the degradation model. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 imagery.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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