Aerial hyperspectral remote sensing classification dataset of Xiongan New Area （Matiwan Village）

Yi CEN; Lifu ZHANG; Xia ZHANG; Yueming WANG; Wenchao QI; Senlin TANG; Peng ZHANG

doi:10.11834/jrs.20209065

Data Paper | Views : 0 下载量: 562 CSCD: 10 更多指标

PDF
Export
Share
Collection
Album

Aerial hyperspectral remote sensing classification dataset of Xiongan New Area （Matiwan Village）
Vol. 24, Issue 11, Pages: 1299-1306(2020)
Published： 07 November 2020 ，
DOI： 10.11834/jrs.20209065

扫描看全文

岑奕，张立福，张霞，王跃明，戚文超，汤森林，张鹏.2020.雄安新区马蹄湾村航空高光谱遥感影像分类数据集.遥感学报，24（11）： 1299-1306

Cen Y，Zhang L F，Zhang X，Wang Y M，Qi W C，Tang S L and Zhang P. 2020. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area （Matiwan Village）. Journal of Remote Sensing（Chinese）， 24（11）：1299-1306
岑奕，张立福，张霞，王跃明，戚文超，汤森林，张鹏.2020.雄安新区马蹄湾村航空高光谱遥感影像分类数据集.遥感学报，24（11）： 1299-1306 DOI： 10.11834/jrs.20209065.

Cen Y，Zhang L F，Zhang X，Wang Y M，Qi W C，Tang S L and Zhang P. 2020. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area （Matiwan Village）. Journal of Remote Sensing（Chinese）， 24（11）：1299-1306 DOI： 10.11834/jrs.20209065.

摘要

高光谱遥感影像分类数据集主要用于辅助高光谱遥感分类算法的精度验证、效率评价及性能评估，一般包括高光谱遥感影像、影像对应地物类别标注以及相关信息文档等内容。常用的高光谱遥感影像分类数据集以欧美为主，如India Pines、Salinas、KSC等。随着中国高光谱遥感传感器技术发展和学术交流机制的日臻完善，国内也发布了高光谱遥感分类数据集，如江苏省常州的茶树数据集。较于欧美高光谱遥感分类数据集的广泛应用，中国高光谱遥感分类数据集的发布与应用仍偏少。近年来，中国高质量高光谱遥感数据获取能力大幅增强，提升了中国高光谱遥感共享数据源的数量及质量，为促进中国高光谱遥感应用研究及业务化能力提供了支撑。本分类数据集包括雄安新区马蹄湾村高光谱影像数据，由中国科学院上海技术物理研究所研制高分专项航空系统全谱段多模态成像光谱仪采集，光谱范围为400—1000 nm，波段250个，影像大小为3750×1580像元，空间分辨率0.5 m；同步实地调研地类分布19种，包括水稻茬、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。利用随机森林分类方法对该数据进行了分类验证，分类精度可达97%。该数据集（下载方式：

http：//www.hrs-cas.com/a/share/shujuchanpin/2019/

http://www.hrs-cas.com/a/share/shujuchanpin/2019/

0501/1049.html）可为中国经济作物高光谱精细分类研究提供良好的数据支持，更可为中国高光谱遥感载荷业务化应用发展提供有力促进。

Abstract

An aerial hyperspectral remote sensing dataset plays an important role on the research of hyperspectral images

including classification. However

few studies have been conducted on the establishment of a standard hyperspectral dataset. This study introduced a standard hyperspectral dataset that includes a hyperspectral remote sensing image

a land cover map

and sensor parameters. This dataset was acquired by using a newly designed airborne hyperspectral sensor accompanied with synchronous ground survey experiments.

An aerial hyperspectral remote sensing image of Xiongan New Area was acquired using the visible and near-infrared imaging spectrometer designed by Shanghai Institute of Technical Physics

Chinese Academy of Sciences on October 2017. The total field of view angle of the spectrometer is 40.6°

the instantaneous field of view is 0.25 mrad

the effective push-scan pixel is 2834

and the maximum speed-to-height ratio is 0.04. The flight height is 2000 m

and the flight areas cover the Xiong County

An County

Rong County

and Baiyangdian Lake. The east-west length is 48 km

the north-south width is 27.5 km

and the total area is 1320 km

. Twenty-one flight lines are found on the east-west direction

and Matiwan Village is located in the 10th and 11th flight lines. The flying weather is clear and cloudless

and the visibility condition is good. Radiation correction

geometric correction

and image mosaic and clipping were conducted before data classification.

The spectral range of the aerial hyperspectral remote sensing image of Xiongan New Area (Matiwan Village) is 400—1000 nm

with 250 bands and a spatial resolution of 0.5 m. The image size is 3750 × 1580 pixels. The land cover types labeled here are 19

which are mainly cash crops.

The aerial hyperspectral remote sensing image of Matiwan Village in Xiongan New Area was classified using random forest classification. The first three principal components of the spectrum and its corresponding eight spatial texture features and vegetation indices

such as normalized difference water index and normalized difference vegetation index were utilized. The total classification accuracy is 97%

and the kappa coefficient is 0.98. In accordance with the confusion matrix

the confusion of

Robinia pseudoacacia

pear tree

and

Acer

complex is serious. This condition causes the low classification accuracy of

Robinia pseudoacacia

An aerial hyperspectral remote sensing dataset of Xiongan New Area (Matiwan Village) with high spatial and spectral resolution was used in this study. The dataset was classified using random forest classification. The total classification accuracy is 97%. This finding shows that the dataset can provide good data support for hyperspectral classification research and can serve as reference for the design and demonstration of hyperspectral imaging spectrometer.

关键词

高光谱遥感雄安新区航空影像影像分类数据集

Keywords

hyperspectral remote sensingXiongan New Areaaerial imageimage classificationdataset

references

Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256

杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]

Huang S G, Zhang H Y and Pižurica A. 2017. A robust sparse representation model for hyperspectral image classification. Sensors, 17(9): 2087 [DOI: 10.3390/s17092087http://dx.doi.org/10.3390/s17092087]

Jia J X, Wang Y M, Cheng X Y, Yuan L Y, Zhao D, Ye Q, Zhuang X Q, Shu R and Wang J Y. 2018. Destriping algorithms based on statistics and spatial filtering for visible-to-thermal infrared pushbroom hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 4077-4091 [DOI: 10.1109/TGRS.2018.2889731http://dx.doi.org/10.1109/TGRS.2018.2889731]

Jin Q H, Zhu L L, Zhang L X and Jiang Y H. 2009. Examples of using hyperspectral remote sensing technology for mineral resource evaluation and mining environment monitoring. Geological Bulletin of China, 28(2): 278-284

金庆花, 朱丽丽, 张立新, 江永宏. 2009. 矿产资源评价与矿山环境监测中高光谱遥感技术方法应用的实例. 地质通报, 28(2): 278-284 [DOI: 10.3969/j.issn.1671-2552.2009.02.021http://dx.doi.org/10.3969/j.issn.1671-2552.2009.02.021]

Li X K, Wu T X, Liu K, Li Y and Zhang L F. 2016. Evaluation of the chinese fine spatial resolution hyperspectral satellite TianGong-1 in urban land-cover classification. Remote Sensing, 8(5): 438 [DOI: 10.3390/rs8050438http://dx.doi.org/10.3390/rs8050438]

Tong Q X, Xue Y Q and Zhang L F. 2014. Progress in hyperspectral remote sensing science and technology in china over the past three decades. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 70-91 [DOI: 10.1109/JSTARS.2013.2267204http://dx.doi.org/10.1109/JSTARS.2013.2267204]

Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689-707

童庆禧, 张兵, 张立福. 2016. 中国高光谱遥感的前沿进展. 遥感学报, 20(5): 689-707 [DOI: 10.11834/jrs.20166264http://dx.doi.org/10.11834/jrs.20166264]

Wang Y M, Jia J X, He Z P and Wang J Y. 2016. Key technologies of advanced hyperspectral imaging system. Journal of Remote Sensing, 20(5): 850-857

王跃明, 贾建鑫, 何志平, 王建宇. 2016. 若干高光谱成像新技术及其应用研究. 遥感学报, 20(5): 850-857 [DOI: 10.11834/jrs.20166206http://dx.doi.org/10.11834/jrs.20166206]

Zhang L F, Jiao W Z, Zhang H M, Huang C P and Tong Q X. 2017. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sensing of Environment, 190: 96-106 [DOI: 10.1016/j.rse.2016.12.010http://dx.doi.org/10.1016/j.rse.2016.12.010]

Zhang L F, Zhang L P, Tao D C and Huang X. 2012. On combining multiple features for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 50(3): 879-893 [DOI: 10.1109/TGRS.2011.2162339http://dx.doi.org/10.1109/TGRS.2011.2162339]

Zhang X, Sun Y L, Shang K, Zhang L F and Wang S D. 2016. Crop classification based on feature band set construction and object-oriented approach using hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9): 4117-4128 [DOI: 10.1109/JSTARS.2016.2577339http://dx.doi.org/10.1109/JSTARS.2016.2577339]

Zhang X, Zhang B, Zhang L F and Sun Y L. 2017. Hyperspectral remote sensing dataset for tea farm. Global Change Research Data Publishing and Repository

张霞, 张兵, 张立福, 孙艳丽. 2017. 茶树等十种地类高光谱遥感数据集. 全球变化科学研究数据出版系统) [DOI: 10.3974/geodb.2017.03.04.V1http://dx.doi.org/10.3974/geodb.2017.03.04.V1]

Zhao B, Zhong Y F and Zhang L P. 2016. A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 116: 73-85 [DOI: 10.1016/j.isprsjprs.2016.03.004http://dx.doi.org/10.1016/j.isprsjprs.2016.03.004]

Zhong Y F, Wu Y Y, Xu X and Zhang L P. 2015. An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1411-1426 [DOI: 10.1109/TGRS.2014.2340734http://dx.doi.org/10.1109/TGRS.2014.2340734]

Alert me when the article has been cited

提交

Hyperspectral scene classification dataset based on Zhuhai-1 images

Dynamic selection algorithm for collaborative representation of hyperspectral remote sensing based on joint spatial information

Hyperspectral remote sensing imagery classification based on elastic net and low-rank representation

MtSCCD： Land-use scene classification and change-detection dataset for deep learning