Discussion on the method of forming mask image based on ERDAS

doi:10.6046/gtzyyg.2013.02.15

Abstract
References
Related Articles
Metrics

Download: PDF(899 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract The problem as to how to form binary or multi mask image by ERDAS’s Visualization Modeler according to the condition with mult-interference information has been seldom dealt with in previous papers; nevertheless, this problem is really the key to deleting the interference information and extracting the useful information. In this paper, the authors discussed the method for forming binary or multi mask image by ERDAS’s Visualization Modeler, extracted and analyzed the iron-stained and hydroxyl anomaly alteration information based on the multi mask image and principal component analysis technology. The results show that the interference information or the false information can be deleted and the useful information can be extracted from the remote sensing image by masking based on the multi mask image, and that the high quality basic data can be provided for the extraction of the alteration information from the remote sensing image.

Keywords digital nautical chart wetland remote sensing evolution analysis Chongming Dongtan

TP751.1

Issue Date: 28 April 2013

	Service

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

	ZHENG Zongsheng
	ZHOU Yunxuan
	TIAN Bo
	JIANG Xiaoyi
	LIU Zhiguo

Cite this article:

ZHENG Zongsheng,ZHOU Yunxuan,TIAN Bo, et al. Discussion on the method of forming mask image based on ERDAS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(2): 81-86.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.02.15 OR https://www.gtzyyg.com/EN/Y2013/V25/I2/81

[1] 党安荣,王晓栋,陈晓峰,等.ERDAS IMAGINE遥感图像处理方法[M].北京:清华大学出版社,2003:69-94. Dang A R,Wang X D,Chen X F,et al.The ERDAS imagine remote sensing image processing methods[M].Beijing:Tsinghua University Press,2003:69-94.
[2] 张玉君.遥感异常提取方法技术推广教材[M].北京:中国国土资源航空物探遥感中心,2007. Zhang Y J.The promotional materials of remote sensing anomaly extraction technology[M].Beijing:China Aero Geophysical Survey and Remote Sensing Center for Land and Resources,2007.
[3] 苏一鸣,李向前,朱叶飞.ETM⁺/TM蚀变遥感异常提取方法在宁镇地区的应用[J].地质学刊,2009,33(1):84-88. Su Y M,Li X Q,Zhu Y F.Application of ETM⁺/TManomaly extraction method of altered remote sensing in Nanjing to Zhenjiang region[J].Journal of Geology,2009,33(1):84-88.
[4] 张玉君,杨建民,陈薇.ETM⁺/TM蚀变遥感异常提取方法研究与应用——地质依据和波谱前提[J].国土资源遥感,2002,14(4):2-5. Zhang Y J,Yang J M,Chen W.A study of the method for extraction of alteration anomalies from the ETM⁺/TM data and its application:Geologic basis and spectral precondition[J].Remote Sensing for Land and Resources,2002,14(4)2-5.
[5] 陈建平,王倩,董庆吉,等.青海沱沱河地区遥感蚀变信息提取[J].地球科学:中国地质大学学报,2009,34(2):314-318. Chen J P,Wang Q,Dong Q J,et al.Extraction of remote sensing alteration information in Tuotuohe,Qinghai Province[J].Earth Science:Journal of China University of Geosciences,2009,34(2):314-318.
[6] 孙家柄,舒宁,关泽群.遥感原理、方法和应用[M].北京:测绘出版社,1997:107-110. Sun J B,Su N,Guan Z Q.Remote sensing principle,method and application [M].Beijing:Press of Surveying and Mapping,1997:107-110.
[7] 孙家柄.遥感原理与应用[M].武汉:武汉大学出版社,2003:116-141. Sun J B.Remote sensing principle and application[M].Wuhan:Wuhan University Press,2003:116-141.
[8] 郭凯,孙培新,刘卫国.利用ERDAS IMAGINE从遥感影像中提取植被指数[J].西部探矿工程,2005(6):210-212. Guo K,Sun P X,Liu W G.Abstracting vegetation index from remote sensing images using ERDAS IMAGINE[J].West-China Exploration Engineering,2005(6):210-212.
[9] 李明.利用遥感等手段圈定紫金山铜金矿床外围找矿有利区[J].国土资源遥感,2012,24(1):137-142. Li M.The delineation of potential ore-prospecting areas in the Zijinshan copper-gold deposit and its outskirts by using remote sensing and other means[J].Remote Sensing for Land and Resources,2012,24(1):137-142.
[10] 张永庭,张晓东,刘自增,等.宁夏区地质构造与围岩蚀变遥感信息提取[J].国土资源遥感,2012,24(1):132-136. Zhang Y T,Zhang X D,Liu Z Z,et al.The extraction of fault structure and wall rock alteration remote sensing information in Ningxia[J].Remote Sensing for Land and Resources,2012,24(1):132-136.
[11] 况忠,龙胜清,曾禹人,等.黔西南地区遥感构造与金矿的关系及找矿预测[J].国土资源遥感,2012,24(1):160-165. Kuang Z,Long X Q,Zeng Y R,et al.The relationship between remote sensing structures and gold deposits and ore-prospecting prognosis in southwest Guizhou[J].Remote Sensing for Land and Resources,2012,24(1):160-165.

[1]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2]	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.
[3]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4]	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.
[5]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6]	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.
[7]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10]	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.
[11]	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.
[12]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[13]	HE Chenlinqiu, CHENG Bo, CHEN Jinfen, ZHANG Xiaoping. Information extraction methods of coastal wetland based on GF-3 fully polarimetric SAR data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 105-110.
[14]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[15]	LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.

Viewed

Full text

Abstract

Cited

Shared

Discussed