A near-rectangle guided segmentation method for remote sensing images of corn field areas
LIANG Ruofei1, YANG Fengbao1, WANG Yimin2, MENG Yingchen2, WEI Hong3
1. Information and Communication Engineering College, North University of China, Taiyuan 030051, China;
2. Remote Sensing Center of Agriculture, Shanxi Province, Taiyuan 030051, China;
3. School of Systems Engineering, University of Reading, Reading RG6 6 AU, UK
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.
[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.