A near-rectangle guided segmentation method for remote sensing images of corn field areas

doi:10.6046/gtzyyg.2016.03.09

Abstract
References
Related Articles
Metrics

Download: PDF(3233 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Corn field remote sensing images have a mass of end member spectral variability among marginal land area. When traditional method is used for corn block segmentation, it will produce a number of small corn plot areas at the edge and result in statistical errors of the planting area. According to the distribution characteristics of large corn planting area, an near-rectangle guided segmentation method for remote sensing images in corn field areas is proposed. First, the SUSAN(smallest univale segment assimilating nucleus)operator is used for edge detection from GF-1 fusion images. Then, according to the relationship between closed area and external near rectangular, the near rectangle-guided correlation function is built. At last, the near-rectangle guided threshold function is introduced into the graph-based segmentation algorithm to implement the field parcel segmentation of a specific shape. The results were compared with the graph-based segmentation algorithm, the watershed algorithm and the artificial interpretation sample. It is shown that the method proposed in this paper is effective in distinguishing different features, and the negative impact resulting from the endmember spectral variability can be reduced. The segmentation results are more in line with the actual characteristics of corn distribution, conforming with the actual statistics of the corn field area.

Keywords CLUE_S model land use spatial distribution multi-simulation Jinhe watershed

TP751.1

Issue Date: 01 July 2016

	Service

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

	SHI Yunxia
	WANG Fanxia
	WU Zhaopeng

Cite this article:

SHI Yunxia,WANG Fanxia,WU Zhaopeng. A near-rectangle guided segmentation method for remote sensing images of corn field areas[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 53-59.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.03.09 OR https://www.gtzyyg.com/EN/Y2016/V28/I3/53

[1] 陆昳丽.面向对象的农田目标遥感识别与提取研究[D].南京:南京大学,2011:9-20. Lu Y L.Object-based Farmland Recognition and Extraction from High Resolution Remotely Sensed Imagery[D].Nanjing:Nanjing University,2011:9-20.
[2] 彭兴邦,蒋建国.一种基于亮度均衡的图像阈值分割技[J].计算机技术与发展,2006,16(11):10-12. Peng X B,Jiang J G.An image segmentation thresholding method based on luminance proportion[J].Computer Technology and Development,2006,16(11):10-12.
[3] Freixenet J,Munoz X,Raba D,et al.Yet another survey on image segmentation:Region and boundary information integration[M]//Heyden A,Sparr G,Nielsen M,et al.Computer Vision-ECCV 2002.Lecture Notes in Computer Science.Berlin Heidelberg:Springer,2002,2352:408-422.
[4] 田野.面向对象的遥感影像多尺度自适应分割技术[D].上海:上海交通大学,2009. Tian Y.Object-oriented Multi-scale Segmentation of High Resolution Remote Sensing Image[D].Shanghai:Shanghai Jiao Tong University,2009.
[5] Felzenszwalb P F,Huttenlocherr D P.Efficient graph-based image segmentation[J].International Journal of Computer Vision,2004,59(2):167-181.
[6] 杨风暴.红外物理与技术[M].北京:电子工业出版社,2014:180-245. Yang F B.Infrared Physics and Technology[M].Beijing:Publishing House of Electronics Industry,2014:180-245.
[7] 谭玉敏,槐建柱,唐中实.一种边界引导的多尺度高分辨率遥感图像分割方法[J].红外与毫米波学报,2010,29(4):312-315. Tan Y M,Huai J Z,Tang Z S.Edge-guided segmentation method for multiscale and high resolution remote sensing image[J].Journal of Infrared and Millimeter Waves,2010,29(4):312-315.
[8] 罗忠亮.基于改进SUSAN算子的图像边缘检测算法[J].重庆工学院学报:自然科学,2009,23(5):102-106. Luo Z L.Image edge detection algorithm based on improved SUSAN operator[J].Journal of Chongqing Institute of Technology:Natural Science,2009,23(5):102-106.
[9] 苏腾飞,李洪玉,屈忠义.高分辨率遥感图像道路分割算法[J].国土资源遥感,2015,27(3):1-6.doi:10.6046/gtzyyg.2015.03.01. Su T F,LI H Y,Qu Z Y.A study of road segmentation from the high resolution remote sensing image[J].Remote Sensing for Land and Resources,2015,27(3):1-6.doi:10.6046/gtzyyg.2015.03.01.
[10] 黄亮,左小清,冯冲,等.基于Canny算法的面向对象影像分割[J].国土资源遥感,2011,23(4):26-30.doi:10.6046/gtzyyg.2011.04.05. Huang L,Zuo X Q,Feng C,et al.Object-oriented image segmentation based on canny algorithm[J].Remote Sensing for Land and Resources,2011,23(4):26-30.doi:10.6046/gtzyyg.2011.04.05.
[11] Sziranyi T,Shadaydeh M.Improved segmentation of a series of remote sensing images by using a fusion MRF model[C]//Proceedings of the 11th International Workshop on Content-Based Multimedia Indexing(CBMI).Veszprem,Hungary:IEEE,2013:137-142.
[12] 王雷光,刘国英,梅天灿,等.一种光谱与纹理特征加权的高分辨率遥感纹理分割算法[J].光学学报,2009,29(11):3010-3017. Wang L G,Liu G Y,Mei T C,et al.A segmentation algorithm for high-resolution remote sensing texture based on spectral and texture information weighting[J]. Acta Optica Sinica,29(11):3010-3017.
[13] 李华胜,黄平平,苏莹.一种提取遥感影像中道路信息的方法[J].国土资源遥感,2015,27(2):56-62.doi:10.6046/gtzyyg.2015.02.09. Li H S,Huang P P,Su Y.A method for road extraction from remote sensing imagery[J].Remote Sensing for Land and Resources,2015,27(2):56-62.doi:10.6046/gtzyyg.2015.02.09.
[14] Bencherif M A,Bazi Y,Guessoum A,et al.Fusion of extreme learning machine and graph-based optimization methods for active classification of remote sensing images[J].IEEE Geoscience and Remote Sensing Letters,2015,12(3):527-531.
[15] 蔡红玥,姚国清.基于分水岭算法的高分遥感图像道路提取优化方法[J].国土资源遥感,2013,25(3):25-29.doi:10.6046/gtzyyg.2013.03.05. Cai H Y,Yao G Q.Optimized method for road extraction from high resolution remote sensing image based on watershed algorithm[J].Remote Sensing for Land and Resources,2013,25(3):25-29.doi:10.6046/gtzyyg.2013.03.05.
[16] 周绍光,孙金彦,凡莉,等.高分辨率遥感影像的建筑物轮廓信息提取方法[J].国土资源遥感,2015,27(3):52-58.doi:10.6046/gtzyyg.2015.03.10. Zhou S G,Sun J Y,Fan L,et al.Extraction of building contour from high resolution images[J].Remote Sensing for Land and Resources,2015,27(3):52-58.doi:10.6046/gtzyyg.2015.03.10.

[1]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[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]	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.
[4]	WU Jiang, LIU Chun, YING Shen, YU Ting. Spatial delineation methods of urban areas[J]. Remote Sensing for Natural Resources, 2021, 33(4): 89-97.
[5]	WANG Shuang, ZHANG Lei, ZHANG Junyong, WANG Yile. Characteristics of GIS applications in national fitness[J]. Remote Sensing for Natural Resources, 2021, 33(4): 265-271.
[6]	SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[7]	XIAO Dongsheng, LIAN Hong. Population spatialization based on geographically weighted regression model considering spatial stability of parameters[J]. Remote Sensing for Natural Resources, 2021, 33(3): 164-172.
[8]	DENG Xiaojin, JING Changqing, GUO Wenzhang, Yan Yujiang, CHEN Chen. Surface albedos of different land use types in the Junggar Basin[J]. Remote Sensing for Natural Resources, 2021, 33(3): 173-183.
[9]	WANG Qingchuan, XI Yantao, LIU Xinran, ZHOU Wen, XU Xinran. Spatial-temporal response of ecological service value to land use change: A case study of Xuzhou City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 219-228.
[10]	SONG Qi, FENG Chunhui, GAO Qi, WANG Mingyue, WU Jialin, PENG Jie. Change of cultivated land and its driving factors in Alar reclamation area in the past thirty years[J]. Remote Sensing for Land & Resources, 2021, 33(2): 202-212.
[11]	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.
[12]	WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[13]	GAO Wenlong, SU Tengfei, ZHANG Shengwei, DU Yinlong, LUO Meng. Classification of objects and LUCC dynamic monitoring in mining area: A case study of Hailiutu watershed[J]. Remote Sensing for Land & Resources, 2020, 32(3): 232-239.
[14]	Hailing GU, Chao CHEN, Ying LU, Yanli CHU. Construction of regional economic development model based on satellite remote sensing technology[J]. Remote Sensing for Land & Resources, 2020, 32(2): 226-232.
[15]	Ruiqi GUO, Bo LU, Kailin CHEN. Dynamic simulation of multi-scenario land use change based on CLUMondo model: A case study of coastal cities in Guangxi[J]. Remote Sensing for Land & Resources, 2020, 32(1): 176-183.

Viewed

Full text

Abstract

Cited

Shared

Discussed