数据集(库)目录

出版期刊|区域分类

2021年第12期
2019年第02期
数据详情

航空高光谱遥感设备应用于雄安新区(马蹄湾村)影像分类测试数据集


岑奕1张立福*1张霞1王跃明2戚文超1汤森林1张鹏1
1中国科学院空天信息创新研究院,遥感科学国家重点实验室,北京1001012中国科学院上海技术物理研究所,中国科学院空间主动光电技术重点实验室,上海200083

DOI:10.3974/geodb.2021.01.02.V1

出版时间:2021年1月

网页浏览次数:2778       数据下载次数:553      
数据下载量:183244.79 MB      数据DOI引用次数:

关键词:

高光谱遥感,雄安新区,航空影像,分类,遥感学报

摘要:

利用中国科学院上海技术物理研究所研制的航空系统全谱段多模态成像光谱仪对河北省雄安新区马蹄湾村影像进行分类应用试验,得到航空高光谱遥感设备应用于雄安新区(马蹄湾村)影像分类测试数据集。数据集影像光谱范围为400-1000 nm,波段250个,影像大小为3750 x 1580像元,空间分辨率为0.5 m,勾绘标注有经济作物等地物类别19类,具有光谱分辨率高、空间分辨率高、地物类别多等特点。该数据集包括该试验区内:(1)航空高光谱反射率图像;(2)水稻茬、草地、榆树、白蜡、国槐、菜地、杨树、大豆、刺槐、水稻、水体、柳树、复叶槭、栾树、桃树、玉米、梨树、荷叶、建筑物共19种典型地类的ROI数据;(3)地面验证数据。数据集存储为.img格式,由6个数据文件组成,数据量为2.83 GB(压缩为6个文件,1.75 GB)。与该数据集相关的研究论文发表在《遥感学报》2020年第24卷第11期。

基金项目:

科技部项目(2017YFC1500900,2017YFE9124900);国家自然科学基金(41830108)

数据引用方式:

岑奕, 张立福*, 张霞, 王跃明, 戚文超, 汤森林, 张鹏. 航空高光谱遥感设备应用于雄安新区(马蹄湾村)影像分类测试数据集[J/DB/OL]. 全球变化数据仓储电子杂志(中英文), 2021. https://doi.org/10.3974/geodb.2021.01.02.V1.

参考文献:

[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.
     

数据下载:

序号 数据名 数据大小 操作
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
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