USE OF THE MICROCOMPUTER IN AUTOMATED LINEAMENT DETECTION FROM LANDSAT TM DATA

doi:10.6046/gtzyyg.1993.01.06

Download: PDF(356 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract This paper has demonstrated the possibility of developing algorithms to extract lineament information from Landsat TMimagery. We made Use of image smoothing. edge tracing. Hough transformation. inverse Hough transformation etc. in automated lineament detection. After Hough transformation, local maxima are selected in the accumulator array and after inverse Hough transformation, the profile analysis for line segment identification and human-machine interactive methods are used. Some of the rules used by geologists in their image interpretation are applied in automated lineament extraction from digital imagery for improving precision.

Keywords Remotely sensed image Image segmentation Multi-features Object eCognition

Issue Date: 02 August 2011

	Service

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

	CAI Yin-Qiao
	MAO Zheng-Yuan
	XIA Jin-Xia
	LI Fang-Lin
	YANG Dong
	LUO Yang

Cite this article:

CAI Yin-Qiao,MAO Zheng-Yuan,XIA Jin-Xia, et al. USE OF THE MICROCOMPUTER IN AUTOMATED LINEAMENT DETECTION FROM LANDSAT TM DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 1993, 5(1): 29-34.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1993.01.06 OR https://www.gtzyyg.com/EN/Y1993/V5/I1/29

[1] JOHN S. ZELEK. "COMPUTER-AIDED LINEAR PLANIMETRIC FEATURE EXTRACTION". IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1990. 28( 4)

[2] RICHARD B,LAMMERS ETC."AUTOMATING OBJECT REPRESENT ATION OF DRAINAGE BASINS".COMPUTER & GEOSCIENCES, 1990.16( 6): 787～810

[3] ItAMESH KUMAR T. REDDY."DIGITAL ANALYSIS OF LINEAMENTS-A TEST STUDY IN SOUTH INDIA" .Computer & Geosciences, 1991.17( 4):549～559

[4] G. WADGE A1VD A. M. CROSS."IDENTIFICATION AND. ANALYSIS OF THE ALIGNMENTS OF POINT-LIKE FEATURES IN REMOTELY-SENSED IMAGERY".INT. J. REMOTE SENSING, 1989.10(3 )t455～474

[5] J. K. WU, D. S. CHENG, AND D. L. CAL"MODEL-BASED REMOTELY-SENSED IMAGERY INTE-RPRETATION".INT. J. REMOTE SENSING,1988.9 ( 8 ) t 1347～1356

[6] 〔日〕田村秀行等著.《计算机图象处理技术》.北京师大出版社,1988,9

[7] 余松煜等编.《数学图像处理》.电子工业出版社,1989,8

[1]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2]	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.
[3]	ZHANG Chengye, XING Jianghe, LI Jun, SANG Xiao. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 252-257.
[4]	CHEN Jing, CHEN Jingbo, MENG Yu, DENG Yupeng, JIE Yongshi, ZHANG Yi. Detection of wind turbine towers in remote sensing based on YOLOv3 model under scale and density constraints[J]. Remote Sensing for Natural Resources, 2021, 33(3): 54-62.
[5]	CHEN Shuai, ZHAO Wenyu, LIAO Zhongping. Remote sensing identification of black-odor water bodies: A review[J]. Remote Sensing for Land & Resources, 2021, 33(1): 20-29.
[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]	SU Longfei, LI Zhengxuan, GAO Fei, YU Min. A review of remote sensing image water extraction[J]. Remote Sensing for Land & Resources, 2021, 33(1): 9-11.
[8]	WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
[9]	CHEN Pengdi, HUANG Liang, XIA Yan, YU Xiaona, GAO Xiaxia. Detection and recognition of road traffic signs in UAV images based on Mask R-CNN[J]. Remote Sensing for Land & Resources, 2020, 32(4): 61-67.
[10]	JIANG Shan, WANG Chun, SONG Hongli, LIU Yufeng. A study of crop planting type recognition based on SAR and optical remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 105-110.
[11]	ZHANG Peng, LIN Cong, DU Peijun, WANG Xin, TANG Pengfei. Accurate monitoring of ecological redline areas in Nanjing City using high resolution satellite imagery[J]. Remote Sensing for Land & Resources, 2020, 32(3): 157-164.
[12]	Biqing WANG, Wenquan HAN, Chi XU. Winter wheat planting area identification and extraction based on image segmentation and NDVI time series curve classification model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 219-225.
[13]	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.
[14]	Guangyu ZHOU, Bangquan LIU, Dan ZHANG. Target recognition in SAR images based on variational mode decomposition[J]. Remote Sensing for Land & Resources, 2020, 32(2): 33-39.
[15]	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.