Design and development of the shadow detection and compensation system for high-resolution remote sensing images

doi:10.6046/gtzyyg.2015.03.28

Abstract
References
Related Articles
Metrics

Download: PDF(3023 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract Shadow detection and compensation involve such problems as the uncertainty of remote sensing images, the complexity of algorithm and the low degree of automatic extraction. In view of this situation, the authors have designed an integrated experimental system based on the algorithm built for high-resolution remote sensing image shadow detection and compensation on the ArcGIS Engine platform. The system also utilizes Matlab and GDAL. Some key technologies such as data block reading, 2% linear stretch and DLL are used in the shadow detection and compensation system implementation, which solves some problems such as reading large quantities of data, uncertainty of image, and extensibility of the system. The system achieves the integration and optimization of the system, and improves the operating efficiency. Experimental results show that the system performs higher precision and efficiency in shadow detection and compensation for high-resolution remote sensing images such as QuickBird and ZY-3. Therefore, the system can be used for batch processing of remote sensing image data.

Keywords MODIS normalized difference vegetation index(NDVI) HJ-1A CCD rape distribution Hubei

TP79

Issue Date: 23 July 2015

	Service

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

	WANG Kai
	ZHANG Jiahua

Cite this article:

WANG Kai,ZHANG Jiahua. Design and development of the shadow detection and compensation system for high-resolution remote sensing images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 177-181.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.03.28 OR https://www.gtzyyg.com/EN/Y2015/V27/I3/177

[1] 田原.遥感影像的阴影检测算法研究[D].西安:西安电子科技大学,2013. Tian Y.Research on Shadow Detection Algorithm in Remote Sensing Images[D].Xi'an:Xidian University,2013.
[2] 王玥,王树根.高分辨率遥感影像阴影检测与补偿的主成分分析方法[J].应用科学学报,2010,28(2):136-141. Wang Y,Wang S G.Dection and compensation of shadows in high resolution remote sensing images using PCA[J].Journal of Applied Sciences-Electronics and Information Engineering,2010,28(2):136-141.
[3] 虢建宏,田庆久.一种IKONOS影像阴影检测与去除的方法[J].遥感信息,2005(4):32-34. Guo J H,Tian Q J.A shadow detection and removal method for IKONOS images[J].Remote Sensing Information,2005(4):32-34.
[4] 徐秋红,叶勤.一种基于颜色恒常性理论的城市高分辨率遥感影像阴影消除方法[J].遥感信息,2010(4):13-17. Xu Q H,Ye Q.A method of shadow elimination from city high resolution remote sensing images based on colour constancy[J].Remote Sensing Information,2010(4):13-17.
[5] 高贤君,万幼川,郑顺义,等.航空遥感影像阴影的自动检测与补偿[J].武汉大学学报:信息科学版,2012,37(11):1299-1302. Gao X J,Wan Y C,Zheng S Y,et al.Automatic shadow detection and compensation of aeral remote sensing images[J].Geomatics and Information Science of Wuhan University,2012,37(11):1299-1302.
[6] 王蜜蜂.遥感影像的阴影检测与补偿方法研究[D].西安:西安电子科技大学,2013. Wang M F.Research on Shadow Detection and Compensation in Remote Sensing Images[D].Xi'an:Xidian University,2013.
[7] 冈萨雷斯,伍兹,埃丁斯.数字图像处理:MATLAB版[M].北京:电子工业出版社,2005:8-28. Gonzalez R C,Woods R E,Eddins S L.Digital Image Processing Using MATLAB[M].Beijing:Publishing House of Electronics Industry,2005:8-28.
[8] 梁原.基于MATLAB的数字图像处理系统研究[D].长春:长春理工大学,2008:4. Liang Y.The Research of Digital Image Processing Based on MATLAB[D].Changchun:Changchun University of Science and Technology,2008:4.
[9] 王海岗,张坤宇,张雁佳,等.VC与MATLAB混合编程在图像处理中的应用[J].计算机应用与软件,2012,29(1):273-275. Wang H G,Zhang K Y,Zhang Y J,et al.Application of mixed programming with VC and MATLAB in image processing[J].Computer Applications and Software,2012,29(1):273-275.
[10] 李芳,邬群勇,汪小钦.基于GeoRaste的多源遥感数据存储研究[J].测绘科学,2009,4(3):150-151. Li F,Wu Q Y,Wang X Q.Storage of multi-source remote sensing data based on GeoRaster[J].Science of Surveying and Mapping,2009,4(3):150-151.
[11] 牛芩涛,盛业华.GeoTIFF图像文件的数据存储格式及读写[J].四川测绘,2004,27(3):105-108. Niu Q T,Sheng Y H.The storage and read/write of GeoTIFF image file[J].Surveying and Mapping of Sichuan,2004,27(3):105-108.
[12] 靖常峰,刘仁义,刘南.大数据量遥感图像处理系统算法模块的设计及实现[J].浙江大学学报:理学版,2005,32(4):471-474. Jing C F,Liu R Y,Liu N.Design and development of algorithm module for a remote sensing image processing system[J].Journal of Zhejiang University:Science Edition,2005,32(4):471-474.

[1]	HU Yingying, DAI Shengpei, LUO Hongxia, LI Hailiang, LI Maofen, ZHENG Qian, YU Xuan, LI Ning. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1): 210-217.
[2]	ZHANG Aizhu, WANG Wei, ZHENG Xiongwei, YAO Yanjuan, SUN Genyun, XIN Lei, WANG Ning, HU Guang. A hierarchical spatial-temporal fusion model[J]. Remote Sensing for Natural Resources, 2021, 33(3): 18-26.
[3]	WEI Geng, HOU Yuqiao, ZHA Yong. Analysis of aerosol type changes in Wuhan City under the outbreak of COVID-19 epidemic[J]. Remote Sensing for Natural Resources, 2021, 33(3): 238-245.
[4]	WEI Geng, HOU Yuqiao, HAN Jiamei, ZHA Yong. The estimation of PM_2.5 mass concentration based on fine-mode aerosol and WRF model[J]. Remote Sensing for Land & Resources, 2021, 33(2): 66-74.
[5]	CHEN Baolin, ZHANG Bincai, WU Jing, LI Chunbin, CHANG Xiuhong. Historical average method used in MODIS image pixel cloud compensation: Exemplified by Gansu Province[J]. Remote Sensing for Land & Resources, 2021, 33(2): 85-92.
[6]	YANG Huan, DENG Fan, ZHANG Jiahua, WANG xueting, MA Qingxiao, XU Nuo. A study of information extraction of rape and winter wheat planting in Jianghan Plain based on MODIS EVI[J]. Remote Sensing for Land & Resources, 2020, 32(3): 208-215.
[7]	Gang DENG, Zhiguang TANG, Chaokui LI, Hao CHEN, Huanhua PENG, Xiaoru WANG. Extraction and analysis of spatiotemporal variation of rice planting area in Hunan Province based on MODIS time-series data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 177-185.
[8]	Kailun JIN, Lu HAO. Evapotranspiration estimation in the Jiangsu-Zhejiang-Shanghai Area based on remote sensing data and SEBAL model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 204-212.
[9]	Bing ZHAO, Kebiao MAO, Yulin CAI, Xiangjin MENG. Study of the temporal and spatial evolution law of land surface temperature in China[J]. Remote Sensing for Land & Resources, 2020, 32(2): 233-240.
[10]	Yiqiang SHI, Qiuqin DENG, Jun WU, Jian WANG. Regression analysis of MODIS aerosol optical thickness and air quality index in Xiamen City[J]. Remote Sensing for Land & Resources, 2020, 32(1): 106-114.
[11]	Yuqi CHENG, Yuqing WANG, Jingping SUN, Chengfu ZHANG. Temporal and spatial variation of evapotranspiration and grassland vegetation cover in Duolun County, Inner Mongolia[J]. Remote Sensing for Land & Resources, 2020, 32(1): 200-208.
[12]	Kailin LI, Chungui ZHANG, Kuo LIAO, Lichun LI, Hong WANG. Study of remote sensing atmosphere index of Fujian Province[J]. Remote Sensing for Land & Resources, 2019, 31(4): 151-158.
[13]	Linlin WU, Yunlan GUAN, Jiawei LI, Chenxin YUAN, Rui LI. Assessment of Karst rocky desertification based on MODIS: Exemplified by Guizhou Province[J]. Remote Sensing for Land & Resources, 2019, 31(4): 235-242.
[14]	Jiaping WU, Yang ZHANG, Jie ZHANG, Shenglong FAN, Chao YANG, Xiaofang ZHANG. Comparison and analysis of water indexes in muddy coasts based on MODIS data: A case study of the Yellow River Delta coast[J]. Remote Sensing for Land & Resources, 2019, 31(3): 242-249.
[15]	Ying LIU, Hui YUE, Enke HOU. Drought monitoring based on MODIS in Shaanxi[J]. Remote Sensing for Land & Resources, 2019, 31(2): 172-179.

Viewed

Full text

Abstract

Cited

Shared

Discussed