Dataset List


Oceanic region
Data Details

Aerial Hyperspectral Remote Sensing Application Dataset in XiongAn (Matiwan Village) of Hebei Province of China

CEN Yi1ZHANG Lifu*1ZHANG Xia1WANG Yueming2QI Wenchao1TANG Senlin1ZHANG Peng1
1State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China2Key Laboratory of Space Active Opto-Electronics Technology,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China


Published:Jan. 2021

Visitors:183       Data Files Downloaded:104      
Data Downloaded:39038.39 MB      Citations:

Key Words:

hyperspectral remote sensing,Xiongan New Area,Aerial image,classification,Journal of Remote Sensing


Using the visible and near-infrared imaging spectrometer designed by Shanghai Institute of Technical Physics, Chinese Academy of Sciences, to identify the land cover and land use in XiongAn (Matiwan Village) of Hebei Province of China. The spectral range of the aerial hyperspectral remote sensing image is 400-1000 nm, with 250 bands and a spatial resolution of 0.5 m. The image size is 3750 x 1580 pixels. The 19 land cover types were labeled, which are mainly cash crops. The dataset includes: (1) hyperspectral images at Matiwan Village;( 2) ROI data of 19 land cove types including rice, grassland, Elm, etc.; (3) ground validation data. The dataset is archived in .img data format, and consists of 6 data files with data size of 2.83 GB (compressed to 6 data files with 1.75 GB). The analysis paper based on the dataset was published at Journal of Remote Sensing, Vol. 24, No. 11, 2020.

Foundation Item:

Ministry of Science and Technology of P. R. China (2017YFC1500900, 2017YFE9124900); National Natural Science Foundation of China (41830108)

Data Citation:

CEN Yi,ZHANG Lifu*,ZHANG Xia,WANG Yueming,QI Wenchao,TANG Senlin,ZHANG Peng.Aerial Hyperspectral Remote Sensing Application Dataset in XiongAn (Matiwan Village) of Hebei Province of China[DB/OL].Global Change Data Repository,2021.DOI:10.3974/geodb.2021.01.02.V1.


[1] Du, P. J., Xia, J. S., Xue, Z. H., et al. Review of hyperspectral remote sensing image classification [J]. Journal of Remote Sensing, 2016, 20(2): 236-256.
     [2] Huang, S. G., Zhang, H. Y., Pizurica, A. A robust sparse representation model for hyperspectral image classification [J]. Sensors, 2017, 17(9): 2087.DOI: 10.3390/s17092087.
     [3] Jia, J. X., Wang, Y. M., Cheng, X. Y., et al. Destriping algorithms based on statistics and spatial filtering for visible-to-thermal infrared pushbroom hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(6): 4077-4091. DOI: 10.1109/TGRS.2018.2889731.
     [4] Jin, Q. H., Zhu, L. L., Zhang, L. X., et al. Examples of using hyperspectral remote sensing technology for mineral resource evaluation and mining environment monitoring [J]. Geological Bulletin of China, 2009, 28(2): 278-284.
     [5] Li, X. K., Wu, T. X., Liu, K., et al. Evaluation of the Chinese fine spatial resolution hyperspectral satellite TianGong-1 in urban land-cover classification [J]. Remote Sensing, 2016, 8(5): 438. DOI: 10.3390/rs8050438.
     [6] Tong, Q. X., Xue, Y. Q., Zhang, L. F. Progress in hyperspectral remote sensing science and technology in china over the past three decades [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70-91. DOI: 10.1109/JSTARS.2013.2267204.
     [7] Tong, Q. X., Zhang, B., Zhang, L. F. Current progress of hyperspectral remote sensing in China [J]. Journal of Remote Sensing, 2016, 20(5): 689-707.
     [8] Wang, Y. M., Jia, J. X., He, Z. P., et al. Key technologies of advanced hyperspectral imaging system [J]. Journal of Remote Sensing, 2016, 20(5): 850-857.
     [9] Zhang, L. F., Jiao, W. Z., Zhang, H. M., et al. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices [J]. Remote Sensing of Environment, 2017, 190: 96-106. DOI: 10.1016/j.rse.2016.12.010.
     [10] Zhang, L. F., Zhang, L. P., Tao, D. C., et al. On combining multiple features for hyperspectral remote sensing image classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3): 879-893. DOI: 10.1109/TGRS.2011.2162339.
     [11] Zhang, X., Sun, Y. L., Shang, K., et al. Crop classification based on feature band set construction and object-oriented approach using hyperspectral images [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4117-4128. DOI: 10.1109/JSTARS.2016.2577339.
     [12] Zhang, X., Zhang, B., Zhang, L. F., et al. Hyperspectral remote sensing dataset for tea farm [J]. Global Change Data Repository [DB/OL], 2017. DOI: 10.3974/geodb.2017.03.04.V1.
     [13] Zhao, B., Zhong, Y. F., Zhang, L. P. A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery [C]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116: 73-85. DOI: 10.1016/j.isprsjprs.2016.03.004.
     [14] Zhong, Y. F., Wu, Y. Y., Xu, X., et al. An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery [J]. IEEE Transactions on Geoscience and Remote Sensing [J], 2015, 53(3): 1411-1426. DOI: 10.1109/TGRS.2014.2340734.

Data Product:

ID Data Name Data Size Operation
1 Hyperspectral_XiongAn.part1.rar 389120.00KB
2 Hyperspectral_XiongAn.part2.rar 389120.00KB
3 Hyperspectral_XiongAn.part3.rar 389120.00KB
4 Hyperspectral_XiongAn.part4.rar 389120.00KB
5 Hyperspectral_XiongAn.part5.rar 284977.87KB
6 ROI_GroundTruth.rar 96.18KB