遥感领域remote sensing数据集整理-Super resolution超分辨率任务PAN数据集、多光谱数据集、常见遥感数据集汇总梳理

本文主要是介绍遥感领域remote sensing数据集整理-Super resolution超分辨率任务PAN数据集、多光谱数据集、常见遥感数据集汇总梳理，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

遥感-超分-多光谱	数据集	内容	格式	链接	论文	备注
	MSRSD	包括Pleiades、Worldview-2（WV-2）、Worldview-3（WV-3）、Quickbird-2、GeoEye-1和DEIMOS等几个卫星获取的大多数公开可用的甚高分辨率（VHR）卫星图像	\	[2102.09351] A Comprehensive Review of Deep Learning-based Single Image Super-resolution (arxiv.org)	《A comprehensive review on deep learning based remote sensing image super-resolution methods》	Multi-sensor remote sensing dataset 2022CVPR 主要包括VHR级空间分辨率，将图像制备为全色锐化的三条带
	CAVE	由中国科学院遥感与数字地球研究所开发的合成孔径雷达(SAR)和光学影像数据集	ENVI	\	《Single Image Super-Resolution of SAR Images Using a Generative Adversarial Network》	/
	WorldView	由DigitalGlobe公司运营的WorldView系列商业遥感卫星提供的高分辨率多光谱和全色影像数据	GeoTIFF	\	《Pansharpening of WorldView-3 satellite imagery using convolutional neural network》	/
	Landsat	由美国地质调查局(USGS)提供的Landsat卫星影像数据,包括可见光、近红外和热红外等多个波段	GeoTIFF	\	《Hyperspectral Image Super-Resolution: A Review》	/
	Harvard Forest	由哈佛大学提供的新英格兰地区森林多光谱影像数据	GeoTIFF	\	《Hyperspectral super-resolution by coupled spectral unmixing》	/
	SPARCS	包含7种类型的遥感地物类型，提取自 Landsat 8 OLI/TIRS，由University of Tennessee Knoxville于2014年发布	ENVI	SPARCS \| RS-VLMs (irip-buaa.github.io)	《Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing》	/
	NWPU-RESISC45	The NWPU-RESISC45 remote sensing dataset consists of 45 classes of remote sensing scene data, with each class containing 700 images, totaling 31,500 images of size 256 × 256 RGB and spatial resolutions ranging from 0.2 to 30 m. These images from Google Earth are selected from more than 100 countries and regions. The 45 scenario categories are as follows: airplane, airport, baseball diamond, basketball court, beach, bridge, chaparral, church, circular farmland, cloud, commercial area, dense residential, desert, forest, freeway, golf course, ground track field, harbor, industrial area, intersection, island, lake, meadow, medium residential, mobile home park, mountain, overpass, palace, parking lot, railway, railway station, rectangular farmland, river, roundabout, runway, sea ice, ship, snowberg, sparse residential, stadium, storage tank, tennis court, terrace, thermal power station, and wetland.	\	Remote Sensing Image Scene Classification: Benchmark and State of the Art \| IEEE Journals & Magazine \| IEEE Xplore	《Remote Sensing Image Scene Classification: Benchmark and State of the Art》	/
	iSAID	iSAID: The iSAID dataset consists of 2806 images with different sizes and 655,451 annotated instances. Due to the large size of the original images in the iSAID dataset, we have divided them into 800×800800×800 image patches for training and testing. We have created the SR dataset using bicubic and Gaussian blur to get the LR image with 200×200200×200 sizes. The original training set is used as the training set for the SR task. Additionally, the validation set of iSAID is used as the test set for the SR task. The training set contains a total of 27,286 images and the test set contains a total of 9446 images.	\	CVPR 2019 Open Access Repository (thecvf.com)	《iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images》	/
	RSSCN7	草地、森林、农田、停车场、住宅区、工业区和河湖	.jpg	https://hyper.ai/datasets/5440	《Deep learning based feature selection for remote sensing scene classification》	来源于不同季节和天气变化，并以不同的比例进行采样
	AID	\	\	AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification \| IEEE Journals & Magazine \| IEEE Xplore	《AID: A benchmark data set for performance evaluation of aerial scene classification》	/
	RHLAI	\	\	Remote Sensing \| Free Full-Text \| NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction (mdpi.com)	《NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction》	/
	DIV2K	The DIV2K dataset includes 800 training images, 100 validation images, and 100 test images, all of which have 2K resolution. We divided the images into 480 × 480 sub-images with non-overlapping regions, and obtained LR images through bicubic downsampling.	\	CVPR 2017 Open Access Repository (thecvf.com)	《NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study》	/
	OLI2MSI	The OLI2MSI is a real-world remote sensing image dataset, containing 5225 training LR-HR image pairs and 100 test LR-HR image pairs. The HR images have 480 × 480 resolution and the LR images have a resolution of 180 × 180. The LR images are ground images with a spatial resolution of 30 m, captured by the Operational Land Imager Landsat-8 satellite, and the HR images are ground images with a spatial resolution of 10 m, captured by the Multispectral Instrument Sentinel-2 satellite. Since the original scale factor of the dataset is 3, we used bicubic to obtain LR images for other scale factors.	\	Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN \| Journal of Remote Sensing (science.org)	《Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN》	/
	the Kaggle open-source remote sensing competition dataset	\	\	https://www.kaggle.com/c/draper-satellite-image-chronology/data	/	/

遥感-超分-Pan	数据集	内容	格式	链接	论文	备注
	WorldView-3	\	GeoTiff	\	《Pansharpening of WorldView-3 Satellite Imagery Using Convolutional Neural Network》	/
	QuickBird	\	GeoTiff	\	《Pansharpening of QuickBird Satellite Imagery Using the Curvelet Transform》	/
	GaoFen-2	\	GeoTiff	\	《Pansharpening of GaoFen-2 Satellite Imagery Using Deep Learning》	/
	IKONOS	由GeoEye公司运营的IKONOS商业高分辨率遥感卫星提供的全色和多光谱影像数据	GeoTiff	\	《Pansharpening of Multispectral IKONOS Images via IHS and PCA Transformations》	/
	Pléiades	由CNES公司运营的Pléiades商业高分辨率遥感卫星提供的全色和多光谱影像数据	GeoTiff	\	《Pansharpening of Pléiades Satellite Imagery Using Guided Filtering》	/
	PROBA-V	由欧洲空间局运营的PROBA-V中分辨率植被监测卫星的全色和多光谱影像数据	GeoTiff	\	《PROBA-V Image Pansharpening Using Convolutional Neural Networks》	/
	Sentinel-2	由欧洲空间局运营的Sentinel-2高分辨率多光谱成像卫星的全色和多光谱影像数据	GeoTiff	\	《Pansharpening of Sentinel-2 Imagery Using Guided Filtering》	/
	ZY-3	由中国遥感卫星地面站提供的中国ZY-3高分辨率测绘型遥感卫星的全色和多光谱影像数据	GeoTiff	\	《ZY-3 Satellite Pansharpening Using Convolutional Neural Networks》	/
	DubaiSat-2	由阿联酋空间局运营的DubaiSat-2高分辨率遥感卫星的全色和多光谱影像数据	GeoTiff	\	《Pansharpening of DubaiSat-2 Imagery Using Deep Learning》	/
	COWC	COWC: The COWC is a large dataset of annotated cars from overhead, which consists of images from Selwyn in New Zealand, Potsdam and Vaihingen in Germany, Columbus and Utah in the United States, and Toronto in Canada. We crop the image to 256×256256×256 and randomly select 80% images in Potsdam for training, 10% images in Potsdam for validating, and others for testing. The LR images of the COWC dataset have a size of 64×6464×64 and 32×3232×32, corresponding to ×4×4 and ×8×8 upscale factor SR tasks, respectively.	\	A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning \| SpringerLink	《A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning》	/
	UCMERCED	he UCMERCED remote sensing dataset comprises 21 classes, each comprising 100 images, resulting in 2100 images of size 256 × 256 RGB and a spatial resolution of approximately 0.3 m. These are USGS aerial images from 21 U.S. regions. The 21 classes are as follows: agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts.	\	Bag-of-visual-words and spatial extensions for land-use classification \| Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems (acm.org)	《Bag-of-visual-words and spatial extensions for land-use classification》	/

遥感	数据集	内容	格式	链接	论文	备注
	UCAS-AOD	600 张飞机 & 310 张车辆图像	.png	https://hyper.ai/datasets/5419	《Orientation Robust Object Detection in Aerial Images Using Deep Convolutional Neural Network》	用于飞机和车辆检测，数据集中物体方向分布均匀
	Inria Aerial Image Labeling Dataset	建筑和非建筑（语义分割）	GeoTiff	https://hyper.ai/datasets/5428	\	用于城市建筑物检测的遥感图像数据集
	RSOD-Dataset	飞机、操场、立交桥和油桶四类目标	.jpg	https://hyper.ai/datasets/5425	\	用于遥感图像中物体检测的数据集
	NWPU VHR-10	11类，飞机、舰船、油罐、棒球场、网球场、篮球场、田径场、港口、桥梁和汽车	.jpg	https://hyper.ai/datasets/5422	\	用于空间物体检测的 10 级地理遥感数据集
	RSC11 Dataset	包含11 类场景图像，密林、疏林、草原、港口、高层建筑、低层建筑、立交桥、铁路、居民区、道路、储罐	.tif	https://hyper.ai/datasets/5443	\	一个遥感影像数据集，来源于Google Earth的高分辨率遥感影像，空间分辨率为0.2米
遥感资源大放送（下）\| 11 个经典遥感数据集_遥感影像建筑物数据集-CSDN博客