THE APPLICATION OF SLAR IMAGE IN RESEARCH GEOLOGICAL STRUCTURE IN THE EAST PART OF XINJIANG

doi:10.6046/gtzyyg.1992.02.06

Download: PDF(428 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

This thesis deal concisely with the basic characteristic and interpretation marks of X-band synthetic aperture SCARimages. By means of interpreting SLANimages of semi-overcast and overcast region of dry-gebi in the east part of Xin Jiang, the subsurface structures, such as fold, fracture, ring- structure and so on, is effectively studied and the information about that region is enriched. as a result, a further point is raised that the Longitudinal eune of KUMUTAGEis a rising neotectonic.

Keywords High resolution remote sensing Image classification Object - oriented approach Invariant moments

Issue Date: 02 August 2011

	Service

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

	XU Hai-Qing
	LI Pei-Jun
	CHEN Yi
	WU Bao-Jie
	JI Mei-Xiu
	YANG Hua

Cite this article:

XU Hai-Qing,LI Pei-Jun,CHEN Yi, et al. THE APPLICATION OF SLAR IMAGE IN RESEARCH GEOLOGICAL STRUCTURE IN THE EAST PART OF XINJIANG[J]. REMOTE SENSING FOR LAND & RESOURCES, 1992, 4(2): 31-36.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1992.02.06 OR https://www.gtzyyg.com/EN/Y1992/V4/I2/31

[1]	BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[2]	HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[3]	HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[4]	SUN Ke. Remote sensing image classification based on super pixel and peak density[J]. Remote Sensing for Land & Resources, 2020, 32(4): 41-45.
[5]	CAI Zhiling, WENG Qian, YE Shaozhen, JIAN Cairen. Remote sensing image scene classification based on Inception-V3[J]. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
[6]	WU Tong, PENG Ling, HU Yuan. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
[7]	Chunsen ZHANG, Rongrong WU, Guojun LI, Weihong CUI, Chenyi FENG. High resolution remote sensing image object change detection based on box-plot method[J]. Remote Sensing for Land & Resources, 2020, 32(2): 19-25.
[8]	Pengyan HUANG, Lijing BU, Yongliang FAN. Integrating visual features in polarimetric SAR image classification[J]. Remote Sensing for Land & Resources, 2020, 32(2): 88-93.
[9]	Lihua FU, Ce ZHANG. Study of ore control information in Rongle area of Tibet based on high resolution remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(1): 98-105.
[10]	Yufeng LIU, Ying PAN, Hu LI. *Study of crown information extraction of Picea schrenkiana var. tianschanicabased on high-resolution satellite remote sensing data*[J]. Remote Sensing for Land & Resources, 2019, 31(4): 112-119.
[11]	Benzuo YAO, Fang HE. Spatial and spectral feature hierarchical fusion for hyperspectral image feature extraction[J]. Remote Sensing for Land & Resources, 2019, 31(3): 59-64.
[12]	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.
[13]	Qifang XIE, Guoqing YAO, Meng ZHANG. Research on high resolution image object detection technology based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2019, 31(2): 38-43.
[14]	Ling CHEN, Jia JIA, Haiqing WANG. An overview of applying high resolution remote sensing to natural resources survey[J]. Remote Sensing for Land & Resources, 2019, 31(1): 1-7.
[15]	Wanjun LIU, Tianhui LI, Haicheng QU. Hyperspectral similar sample classification algorithm based on Fisher criterion and TrAdaboost[J]. Remote Sensing for Land & Resources, 2018, 30(4): 41-48.