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

空-天-地一体化智能数字基座关键技术研究与行业应用专题 | 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
- SW-SRD：新疆沙湾地区遥感图像超空间分辨率重建数据集
- 新疆大学学报（自然科学版中英文） 2025年42卷第4期页码：444-456
- 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：
  
  国家自然科学基金“面向数字孪生农业的空-天-地多模态数据融合关键技术研究”（62266043）;新疆维吾尔自治区杰出青年科学基金“基于多时相协同变化检测的基本农田遥感监测研究”（2023D01E01）;新疆维吾尔自治区青年拔尖人才项目“基于多源遥感大数据的粮食安全关键技术研究”（2023TSYCCX0043）;天山创新团队“面向农业的天地协同水资源时空精准调度研究及应用”（2023D14012）;新疆维吾尔自治区重点研发专项“边防异常信息多元感知与智能监测研究”（2023B01029-1,2023B01029-2）;新疆维吾尔自治区自然科学基金“基于深度学习的多源数据融合技术在新疆棉田识别与产量预测中的应用研究”（2022D01B123）
- DOI：10.13568/j.cnki.651094.651316.2024.12.18.0002
  中图分类号： TP751
- 纸质出版：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.

关键词

Keywords

references

LI Z,WANG Y C,ZHANG N,et al.Deep learning-based object detection techniques for remote sensing images:A survey[J].Remote Sensing,2022,14(10):2385.

YUAN X H,SHI J F,GU L C.A review of deep learning methods for semantic segmentation of remote sensing imagery[J].Expert Systems with Applications,2021,169:114417.

KUSSUL N,LAVRENIUK M,SKAKUN S,et al.Deep learning classification of land cover and crop types using remote sensing data[J].IEEE Geoscience and Remote Sensing Letters,2017,14(5):778-782.

TONG X Y,XIA G S,LU Q K,et al.Land-cover classification with high-resolution remote sensing images using transferable deep models[J].Remote Sensing of Environment,2020,237:111322.

NGUYEN T T,HOANG T D,PHAM M T,et al.Monitoring agriculture areas with satellite images and deep learning[J].Applied Soft Computing,2020,95:106565.

WANG X,YI J L,GUO J,et al.A review of image super-resolution approaches based on deep learning and applications in remote sensing[J].Remote Sensing,2022,14(21):5423.

安成邦，张曼，王伟，等．新疆地理环境特征以及农牧格局的形成[J]．中国科学：地球科学，2020,50(2):295-304.AN C B,ZHANG M,WANG W,et al.The pattern of Xinjiang physical geography and its relationship with the temporal-spatial distribution of agriculture and husbandry[J].Scientia Sinica:Terrae,2020,50(2):295-304.(in Chinese)

HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).June 27-30,2016.Las Vegas,NV,USA.IEEE,2016:770-778.

ZHONG X W,QIAN Y R,LIU H,et al.Attention FPNet:Two-branch remote sensing image pansharpening network based on attention feature fusion[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021,14:11879-11891.

MU K,ZHANG Z Y,QIAN Y R,et al.SRT:A spectral reconstruction network for GF-1 PMS data based on transformer and ResNet[J].Remote Sensing,2022,14(13):3163.

WANG Z H,CHEN J,HOI S C H.Deep learning for image super-resolution:A survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(10):3365-3387.

SHOCHER A,COHEN N,IRANI M.Zero-shot super-resolution using deep internal learning[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.June 18-23,2018.Salt Lake City,UT,USA.IEEE,2018:3118-3126.

LIU Z P,TANG H,FENG L,et al.China Building Rooftop Area:The first multi-annual(2016―2021)and high-resolution(2.5 m)building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery[J].Earth System Science Data,2023,15(8):3547-3572.

ZHANG L,LI X,HE D L,et al.LMR:A large-scale multi-reference dataset for reference-based super-resolution[C]//2023IEEE/CVF International Conference on Computer Vision(ICCV).October 1-6,2023.Paris,France.IEEE,2023:13072-13081.

JOZE H R,ZHARKOV I,POWELL K,et al.ImagePairs:Realistic super resolution dataset via beam splitter camera rig[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).June 14-19,2020.Seattle,WA,USA.IEEE,2020:2190-2200.

YANG Y,NEWSAM S.Bag-of-visual-words and spatial extensions for land-use classification[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems.San Jose California.ACM,2010:270-279.

HAN W,ZHANG X H,WANG Y,et al.A survey of machine learning and deep learning in remote sensing of geological environment:Challenges,advances,and opportunities[J].ISPRS Journal of Photogrammetry and Remote Sensing ,2023,202:87-113.

DABOVE P,DAUD M,OLIVOTTO L.Revolutionizing urban mapping:Deep learning and data fusion strategies for accurate building footprint segmentation[J].Scientific Reports,2024,14(1):13510.

YOU X Z,MENG J H,ZHANG M,et al.Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method[J].Remote Sensing,2013,5(7):3190-3211.

ZHANG J C,HUANG Y B,PU R L,et al.Monitoring plant diseases and pests through remote sensing technology:A review[J].Computers and Electronics in Agriculture ,2019,165:104943.

SISHODIA R P,RAY R L,SINGH S K.Applications of remote sensing in precision agriculture:A review[J].Remote Sensing,2020,12(19):3136.

LI M Y,WU P H,WANG B,et al.A deep learning method of water body extraction from high resolution remote sensing images with multisensors[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2021,14:3120-3132.

ZHANG R,ISOLA P,EFROS A A,et al.The unreasonable effectiveness of deep features as a perceptual metric[C]//2018IEEE/CVF Conference on Computer Vision and Pattern Recognition.June 18-23,2018.Salt Lake City,UT,USA.IEEE,2018:586-595.

DING K Y,MA K D,WANG S Q,et al.Image quality assessment:Unifying structure and texture similarity[J].IEEE Transactions on Pattern Analysis and Machine Intelligence ,2022,44(5):2567-2581.

WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:From error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.

KEYS R.Cubic convolution interpolation for digital image processing[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1981,29(6):1153-1160.

LIM B,SON S,KIM H,et al.Enhanced deep residual networks for single image super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).July 21-26,2017.Honolulu,HI,USA.IEEE,2017:1132-1140.

LEI S,SHI Z W.Hybrid-scale self-similarity exploitation for remote sensing image super-resolution[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:5401410.

MEI Y Q,FAN Y C,ZHOU Y Q.Image super-resolution with non-local sparse attention[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).June 20-25,2021.Nashville,TN,USA.IEEE,2021:3516-3525.

LEI S,SHI Z W,MO W J.Transformer-based multistage enhancement for remote sensing image super-resolution[J].IEEE Transactions on Geoscience and Remote Sensing ,2022,60:5615611.

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