Scene classification dataset using the Tiangong-1 hyperspectral remote sensing imagery and its applications

被引：0
|
作者：
Liu K. [1 ]
Zhou Z. [1 ]
Li S. [1 ]
Liu Y. [1 ]
Wan X. [1 ]
Liu Z. [1 ]
Tan H. [1 ]
Zhang W. [1 ]
机构：
[1] Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing
来源：
Yaogan Xuebao/Journal of Remote Sensing | 2020年 / 24卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Data set; Deep learning; Hyperspectral Imager; Scene classification; Tiangong-1;
D O I：
10.11834/jrs.20209323
中图分类号：
学科分类号：
摘要：
Remote sensing image scene classification is an important means of remote sensing image interpretation, which has important application value in land and resources investigation, ecological environment monitoring, disaster assessment, target interpretation and so on. In recent years, deep learning has become a research hotspot in the field of remote sensing scene classification, and data set is the basis for its development. Most of the existing remote sensing scene classification datasets are true color images with single scale and less spectral information. And other hyperspectral data sets have relatively small data coverage. The data of Tiangong-1 Hyperspectral Imager has the characteristics of high spatial resolution, high hyperspectral resolution and wide coverage. It can be used for comprehensive feature extraction and analysis of spectral spatial information, which can provide more abundant data sources for remote sensing image classification application research, and make up for the deficiency of spectral information and limited application of common remote sensing scene classification data sets. In this study, based on the high-quality data acquired by Tiangong-1 Hyperspectral Imager, Tiangong-1 Hyperspectral Remote Sensing Scene Classification data set (TG1HRSSC) is produced through radiation correction, geometric correction, spatial clipping, band screening, and data quality analysis and control. The dataset, which contains the 204 hyperspectral multiresolution image data of nine typical scenes (e.g., city, farmland, forest, pond culture, desert, lake, river and airport), is published and shared in the Space Application Data Promoting Service Platform for China Manned Space Engineering (http://www.msadc.cn [2019-09-10]). The dataset includes one band of 5 m resolution full spectrum, 54 bands of 10 m resolution visible and near-infrared spectrum, and 52 bands of 20 m resolution short-wave infrared spectrum. In addition, this paper describes and analyzes the data set from four aspects: scene distribution, time distribution, spectral distribution and scale distribution. In order to test the application effect of data classification, three classical convolution neural networks (VGG-VD-16, AlexNet and GoogleLeNet) are selected to train the data sets by transfer learning. The overall classification accuracy is 91.52 ± 0.60, 90.47 ± 0.23 and 89.12 ± 0.34, respectively. Results show that the scene classification of the dataset is effective. In following research, the network model can be designed to make full use of the multi-spectral characteristics of the data to achieve more accurate scene classification, and to improve the generalization ability of existing models by using the characteristics of multi-scale data. The data set (TG1HRSSC) has the advantages of hyperspectral, high spatial resolution and multi-scale. The abundant spectral information and fine spatial information provide data support for the research of target recognition of fine ground objects, remote sensing scene classification, remote sensing semantic understanding and other applications, which has unique value and application prospects. © 2020, Science Press. All right reserved.
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页码：1077 / 1087
页数：10
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