THE PETROLEUM GEOLOGICAL ANALYSIS OF REMOTE SENSING IMAGE OF LIAOHE BASIN AND ITS NEAR REGIONS

doi:10.6046/gtzyyg.1990.01.03

Download: PDF(2474 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The images of different Kinds can be obtained by using different methods in processing satelaite data. The images of Liaohe basin, produced by a combined processing method that is linear stretch -histogram adjust-local entrance-colors, are very effective in understanding the relief and structural features of pre-Tertiary base of the area. The geological interpretation of the images confirms that the northeastern (including north-north-eastern) linear structures are dominant in the basin. However, the remote sensing data show the effect of the northwestern linear structures on the formation of the basin. The geological interpretation also reveals that the ring image of the basin has two remarkable featuers. 1). the light tone mainly represents positive structures; 2). the dark tone mainly represents negative structures. Because the known oilfields have bright light tone ring or lump shape, if additional geological data are also Syntheticalay analyzed, new oilfields can be Predicted by the method of analogy.

Keywords Decision tree classification Orthophoto rectification Shape index

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	YANG Qiang
	ZHANG Zhi
	LI Xing-li
	WANG Yan-chun

Cite this article:

YANG Qiang,ZHANG Zhi,LI Xing-li, et al. THE PETROLEUM GEOLOGICAL ANALYSIS OF REMOTE SENSING IMAGE OF LIAOHE BASIN AND ITS NEAR REGIONS[J]. REMOTE SENSING FOR LAND & RESOURCES, 1990, 2(1): 13-19.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1990.01.03 OR https://www.gtzyyg.com/EN/Y1990/V2/I1/13

[1]	Xianyu GUO, Kun LI, Zhiyong WANG, Hongyu LI, Zhi YANG. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM+SFS strategy[J]. Remote Sensing for Land & Resources, 2018, 30(4): 20-27.
[2]	WANG Jinjie, DING Jianli, ZHANG Cheng, CHEN Wenqian. Method of water information extraction by improved SWI based on GF-1 satellite image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 29-35.
[3]	YANG Qingshan, ZHANG Hua. A new method for classification of high spatial resolution remotely sensed imagery based on fusion of shape and spectral information of pixels[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 64-70.
[4]	YANG Yuhui, YAN Meichun, LI Zhijia, YU Qing, CHEN Beibei. Classification model for "same subject with different spectra" on complicated surface in Southern hilly areas[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 79-83.
[5]	LIU Lei, ZANG Shuying, SHAO Tiantian, WEI Jinhong, SONG Kaishan. Characterization of lake morphology in China using remote sensing and GIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 92-98.
[6]	WAN Jianhua, LI Mei, REN Guangbo, MA Yi. Efficient method for updating coastal wetland map based on change detection technology[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 85-90.
[7]	SUN Ming, SHEN Wei-shou, XIE Min, LI Hai-dong, GAO Fei. The Identification of Grassland Types in the Source Region of the Yarlung Zangbo River Based on Spectral Features[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(1): 83-89.
[8]	WANG Xun, XU Dan-Dan, LI Wen-Long. IMAGE EXTRACTION OF MAQU WETLANDS AND ACCURACY ANALYSIS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(4): 96-100.
[9]	YANG Qiang, ZHANG Zhi. THE REMOTE SENSING EXTRACTION METHOD FOR THE MINING AREA AND THE SOLID WASTE IN THE BAOKANG PHOSPHORITE ORE DISTRICT, HUBEI PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(2): 87-90.