SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集

邓峰良; 钱育蓉; 吴同铨; 范迎迎; 白璐; 王元旭

doi:10.13568/j.cnki.651094.651316.2024.12.18.0002

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SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集

更新时间：2026-01-22

- SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 42, Issue 4, Pages: 444-456(2025)
- 作者机构：
  
  1. 新疆大学软件学院新疆大数据与智能软件工程研究中心
  2. 新疆大学软件工程重点实验室
  3. 丝路多语言认知计算国际合作联合实验室
  4. 新疆大学计算机科学与技术学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2024.12.18.0002
  CLC： TP751
- Published：2025
- 稿件说明：
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[1]邓峰良,钱育蓉,吴同铨,等.SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集[J].新疆大学学报(自然科学版中英文),2025,42(04):444-456.
[1]邓峰良,钱育蓉,吴同铨,等.SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集[J].新疆大学学报(自然科学版中英文),2025,42(04):444-456. DOI： 10.13568/j.cnki.651094.651316.2024.12.18.0002.

DOI：10.13568/j.cnki.651094.651316.2024.12.18.0002.

摘要

随着遥感技术的飞速发展，高分辨率遥感图像在环境监测、农业资源管理等领域的需求不断增加．然而，现有数据集（如CBRA、SpaceNet等）缺乏更高分辨率的农田区域数据，制约了超分辨率重建技术在复杂地表环境中的应用．为填补这一空白，提出了SW-SRD(Shawan Super-spatial Resolution Dataset)超空间分辨率数据集．该数据集基于中国新疆沙湾地区的高分一号卫星图像，包含2 m全色图像与8 m多光谱图像，通过数据融合生成2 m分辨率的多光谱图像．SW-SRD数据集具有以下特点：1）精确对齐的高低分辨率图像对，保证超分模型训练的准确性；2）多光谱波段支持，增强遥感算法的光谱信息利用；3）涵盖农田、建筑物、沙漠、山脉等多样地表特征，为复杂环境下算法研究提供丰富场景．为验证SW-SRD数据集的有效性，选择了多种经典及先进的超分辨率重建方法进行了实验对比．在各项指标中，SW-SRD数据集在农田、建筑物、荒漠等复杂场景的细节还原和纹理表现方面均显著优于其他数据集．

Abstract

With the rapid development of remote sensing technology

the demand for high-resolution remote sensing images in the fields of environmental monitoring and agricultural resource management is increasing. However

existing datasets

such as CBRA and SpaceNet

lack sufficient higher-resolution farmland regional data

which restricts the application of super-resolution reconstruction technology in complex surface environments. To fill this gap

proposes the SW-SRD(Shawan Super-spatial Resolution Dataset) super-spatial resolution dataset. This dataset is based on the Gaofen-1 satellite images of Shawan

Xinjiang of China

and contains 2 m panchromatic images and 8 m multispectral images. Multispectral images with a resolution of 2 m are generated through data fusion. The SW-SRD dataset has the following characteristics: 1) Precisely aligned high-and low-resolution image pairs to ensure the accuracy of super-resolution model training; 2) Multispectral band support to enhance the spectral information utilization of remote sensing algorithms; 3) Covering a variety of surface features such as farmland

buildings

deserts and mountains

providing rich scenarios for algorithm research in complex environments. To verify the effectiveness of the SW-SRD dataset

a variety of classic and advanced super-resolution reconstruction methods are selected for experimental comparison. In various indicators

the SW-SRD dataset is significantly superior to other datasets in detail restoration and texture representation of complex scenes

such as farmland

buildings and deserts.

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Keywords

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