Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    1991, Vol. 3 Issue (3) : 37-42     DOI: 10.6046/gtzyyg.1991.03.05
Technology and Methodology |
FEATURES AND CORRECTION OF THE SYSTEMATIC DISTORTION IN PANORAMIC SCANNER IMAGERY
Tan Haiqiao
China University of Mining & Technology
Download: PDF(1108 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  After briefly describing the features of geometric distortions in remote sensing imagery the emphasis of this paper is placed on the discussion of the sources, extent and variations of systematic distortions in panoramic scanner images, including Landsat-TM, Landsat-MSS and aerial multispectral scanner images. To calculate quantitatively the distortion extent D, a formula is proposed. An example using the aerial multispectral scanner image from southern Germany shows that the systematic distortions in different parts of the image can be corrected satisfactorly and the edge compression can be compensated relevantly also.
Keywords  Soil erosion      RS      GIS      Changes of soil erosion     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LIU Ke
ZHAO Wen-Ji
HU De-Yong
Li-Zhi-Hua
ZHANG Ling-Ling
SUN Chang-Yu
BAI Kun
SHI Hao
Cite this article:   
LIU Ke,ZHAO Wen-Ji,HU De-Yong, et al. FEATURES AND CORRECTION OF THE SYSTEMATIC DISTORTION IN PANORAMIC SCANNER IMAGERY[J]. REMOTE SENSING FOR LAND & RESOURCES, 1991, 3(3): 37-42.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.1991.03.05     OR     https://www.gtzyyg.com/EN/Y1991/V3/I3/37


[1] 田村秀行等:计算机图像处理技术(赫荣成等译,1988年),303页,北京师范大学出版社,1985
[2] Drury, Imagr internretatioa in geology, pp.243, Allen & Unwin (London), 1987
[3] R. A. Schowengerdt: Techoiques for Image Processing and Classification in Remote Sending,pp.248, Academic Press (London), 1985
[4] Colwell, R.N.: Manual of Remote Sensing (2. ed.), 1983
[5] Schroeder, Zicle and Aufgaben des Flugzeugmess Programms, Albertz, J. & Schroeder, M. (Hrsg.) Barichte zum Symposium Rlugzeugmess Programm, 1978
[6] 地质矿产部地质遥感中心:航空热红外遥感图像集,地质出版社,1988
[1] SUN Yiming, ZHANG Baogang, WU Qizhong, LIU Aobo, GAO Chao, NIU Jing, HE Ping. Application of domestic low-cost micro-satellite images in urban bare land identification[J]. Remote Sensing for Natural Resources, 2022, 34(1): 189-197.
[2] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[3] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[4] WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[5] YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[6] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[7] LI Dong, TANG Cheng, ZOU Tao, HOU Xiyong. Detection and assessment of the physical state of offshore artificial reefs[J]. Remote Sensing for Natural Resources, 2022, 34(1): 27-33.
[8] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[9] QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[10] ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[11] REN Chaofeng, PU Yuchi, ZHANG Fuqiang. A method for extracting match pairs of UAV images considering geospatial information[J]. Remote Sensing for Natural Resources, 2022, 34(1): 85-92.
[12] 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.
[13] CHEN Jie, ZHANG Lifu, ZHANG Linshan, ZHANG Hongming, TONG Qingxi. Research progress on online monitoring technologies of water quality parameters based on ultraviolet-visible spectra[J]. Remote Sensing for Natural Resources, 2021, 33(4): 1-9.
[14] GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[15] GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
Viewed
Full text


Abstract

Cited

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