Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    1999, Vol. 11 Issue (3) : 65-71     DOI: 10.6046/gtzyyg.1999.03.13
New Theories and Methods |
HYPERSPECTRAL DATA PROCESSING AND RESEARCH ON GEOLOGICAL APPLICATION IN MIAOERSHAN DISTRICT, GUANGXI PROVINCE
Liu Dechang1, Xie Hongjie1, Li Jianfeng1, Zhao Yingjun1, Huang Shutao1, Zhang Jinye2, Dong Jishi2, Chen Baoshu2
1. Beijing Research Institute of Uranium Geology, Beijing 100029;
2. Central south Geological Bureau of Nuclear Industry 410011
Download: PDF(736 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Taking Miaoershan district in Guangxi Province of South China as an example, the characteristics and the processing techniques of the airborne hyperspectral data are discussed in detail. A series of programs for strip_removing, registration between bands, tangent correction illumination calibration and relative reflectance calibration has been developed. On the basis of software ENVI (the Environment for Visualizing Images), a field spectral library in the study area has been set up and a method for geometric corrections with satisfactory mosaic images has been developed. Great achievements in some aspects such as pure pixel extraction, spectral unmixing, interpretation, silicified zones and other alteration zones are successfully delineated and specific uranium prospecting areas are proposed.

Keywords Multi-source      Multi-temporal      Object-oriented      Island recognition      COSMO-SkyMed     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LI Min-Guang
LI Ying-Cheng
XUE Yan-Li
YE Dong-Mei
GE Wei-Zhong
LV Yu-Zeng
DING Yun-He
Cite this article:   
LI Min-Guang,LI Ying-Cheng,XUE Yan-Li, et al. HYPERSPECTRAL DATA PROCESSING AND RESEARCH ON GEOLOGICAL APPLICATION IN MIAOERSHAN DISTRICT, GUANGXI PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 1999, 11(3): 65-71.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.1999.03.13     OR     https://www.gtzyyg.com/EN/Y1999/V11/I3/65

1 郑兰芬,王晋年.成像光谱遥感技术及成像光谱信息提取的分析与研究.环境遥感,1992,7(1)
2 王向军.成像光谱信息可视化不断系统:〔硕士论文〕.中国科学院遥感应用研究所,1995
3 Lyon R J P风化及其它类荒摸漆表层对高光谱分辨率反射率的影响(一).环境遥感,1996,11(2)
4 Joseph C H, Chein-I Gsang. Hyperspectural image classificatimr and dimenesionality seduction: An orthogonal subspace projection approch. IFFF Transaction on Geoscience and Resmte Sensing,1994, 32(4):779~784
5 Geotz A, Herring M. The high sesolusion imaging spectsaneter (HIRIS) for EOS. IEEE Transaction on Geagcience and Remote Sensing,1989, 27(2)

[1] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[2] WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[3] QIN Dahui, YANG Ling, CHEN Lunchao, DUAN Yunfei, JIA Hongliang, LI Zhenpei, MA Jianqin. A study on the characteristics and model of drought in Xinjiang based on multi-source data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 151-157.
[4] FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.
[5] LAI Peiyu, HUANG Jing, HAN Xujun, MA Mingguo. An analysis of impacts from water impoundment in Three Gorges Dam Project on surface water in Chongqing area base on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2021, 33(4): 227-234.
[6] CAI Xiang, LI Qi, LUO Yan, QI Jiandong. Surface features extraction of mining area image based on object-oriented and deep-learning method[J]. Remote Sensing for Land & Resources, 2021, 33(1): 63-71.
[7] Bai, Yuying, Chengling, Yanru, Shihu. Different remote sensing image matching methods based on multiple constraints[J]. Remote Sensing for Land & Resources, 2020, 32(3): 49-54.
[8] DONG Jiaji, REN Huazhong, ZHENG Yitong, NIE Jing, MENG Jinjie, QIN Qiming. A study of the livability of urban environment based on multi-source remote sensing data: A case study of Beijing City[J]. Remote Sensing for Land & Resources, 2020, 32(3): 165-172.
[9] SANG Xiao, GUO Qiaozhen, QIAO Yue, WU Huanhuan, ZANG Jinlong. Research on livability in Changzhi City of Shanxi Province based on multi-source data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 200-207.
[10] Jisheng XIA, Mengying MA, Zhongren FU. Extraction of mechanical damage surface using GF-2 remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 26-32.
[11] Linyan FENG, Bingxiang TAN, Xiaohui WANG, Xinyun CHEN, Weisheng ZENG, Zhao QI. Object-oriented rapid forest change detection based on distribution function[J]. Remote Sensing for Land & Resources, 2020, 32(2): 73-80.
[12] Liping YANG, Meng MA, Wei XIE, Xueping PAN. Fusion algorithm evaluation of Landsat 8 panchromatic and multispetral images in arid regions[J]. Remote Sensing for Land & Resources, 2019, 31(4): 11-19.
[13] Weidong ZHAO, Yong ZHENG, Haonan ZHANG, Qiong JIANG, Jiajia WEI. Remote sensing interpretation and spatial distribution characteristics of the Anhui segment of Tanlu fault zone based on multi-source data[J]. Remote Sensing for Land & Resources, 2019, 31(4): 79-87.
[14] Hui HUANG, Xiongwei ZHENG, Genyun SUN, Yanling HAO, Aizhu ZHANG, Jun RONG, Hongzhang MA. Seismic image classification based on gravitational self-organizing map[J]. Remote Sensing for Land & Resources, 2019, 31(3): 95-103.
[15] Yi ZHENG, Yiqiong LIN, Jian ZHOU, Weixiu GAN, Guangxuan LIN, Fanghong XU, Guanghui LIN. Mangrove inter-species classification based on ZY-3 image in Leizhou Peninsula, Guangdong Province[J]. Remote Sensing for Land & Resources, 2019, 31(3): 201-208.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech