启发法优化的农田地区高分遥感影像分割

doi:10.6046/gtzyyg.2017.04.16

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摘要

目前主流的高分遥感影像(high resolution remote sensing image,HRI)分割算法在区域合并顺序的确定中很少考虑区域自身的分割质量信息。针对该问题,提出了一种启发法优化的策略,以提高农田地区HRI的分割精度。首先,提出了区域内和区域间的“均一致模型”,前者是利用区域内光谱变化信息来建模的,后者则综合考虑了区域间多光谱与植被信息提取的边界强度; 其次,将区域内和区域间的均一致模型合并,构建启发法的执行标准; 最后,利用该标准使区域合并的搜索过程能够考虑待合并区域自身的分割质量,从而有效抑制过分割与亚分割错误。利用2景不同特点的农田地区HRI进行分割实验,并将所提出的启发法与2种新提出的分割算法进行对比分析。对分割结果的定量评价结果表明,启发法优化策略可以显著提高农田地区HRI的分割精度。

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关键词 ：地质灾害体, 变化检测, 变化量估算

Abstract：

Many mainstream segmentation algorithms for high resolution remote sensing image (HRI)rarely consider the segmentation quality in their region merging process. In order to solve this problem, this paper proposed a strategy to optimize heuristics with the purpose of enhancing segmentation accuracy of HRI captured over agricultural areas. Intra- and inter- region homogeneity models were firstly proposed, with the former constructed upon within-region spectral variance, and the latter considering edge strength extracted from multi-spectral and vegetation information. The criterion of the proposed heuristics was then constructed by combining the intra- and inter- region homogeneity. The new criterion enables the merging process to take into account the segmentation quality, thus constraining over- and under- segmentation errors effectively. Two scenes of HRI acquired over agricultural areas were utilized for validation experiment, and the performance of the proposed method was compared with other two newly proposed methods. By analyzing the quantitative evaluation of the segmentation results, it is found that the proposed method can remarkably improve the segmentation accuracy of HRI in agricultural landscape.

Key words： geological hazard body change detection estimation of variable quantity

收稿日期: 2016-06-14 出版日期: 2017-12-04

TP751

基金资助:

国家自然科学基金项目“科尔沁沙地典型生态系统水热通量传输机理及其与植被耦合关系试验和模拟研究”(编号: 51569017)、“面向对象的河套灌区遥感作物分类算法研究”(编号: 61701265)、内蒙古自然科学基金项目“半干旱区沙地典型生态系统水热通量传输机理研究”(编号: 2015MS0514)和中国博士后科学基金面上资助项目“西部地区博士后人才资助计划”(编号: 2015M572630XB)共同资助

通讯作者: 张圣微(1979-),男,博士,教授,硕士生导师,主要从事定量遥感、生态水文及气候变化等方面的研究。Email: zsw_imau@163.com。

作者简介: 苏腾飞(1987-),男,硕士,实验师,主要研究方向为面向地理对象影像分析的遥感数据处理算法的设计与实现。Email: stf1987@126.com。

引用本文:

苏腾飞, 张圣微, 李洪玉. 启发法优化的农田地区高分遥感影像分割[J]. 国土资源遥感, 2017, 29(4): 106-113.
SU Tengfei, ZHANG Shengwei, LI Hongyu. Heuristics optimized segmentation of agricultural area for high resolution remote sensing image. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 106-113.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2017.04.16 或 https://www.gtzyyg.com/CN/Y2017/V29/I4/106

[1]　Blaschke T,Hay G J,Kelly M,et al.Geographic object-based image analysis:Towards a new paradigm[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,87:180-191.
[2]　Peña-Barragán J M,Ngugi M K,Plant R E,et al.Object-based crop identification using multiple vegetation indices,textural features and crop phenology[J].Remote Sensing of Environment,2011,115(6):1301-1316.
[3]　Peña J,Gutiérrez P,Hervás-Martínez C H,et al.Object-based image classification of summer crops with machine learning methods[J].Remote Sensing,2014,6(6):5019-5041.
[4]　Kim H O,Yeom J M.Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data[J].International Journal of Remote Sensing,2014,35(19):7046-7068.
[5]　苏腾飞,李瑞平.面向对象的农田信息遥感影像分割算法[J].测绘科学,2016,41(3):49-53.
Su T F,Li R P.An object-oriented segmentation method for RS imagery of cropland information[J].Science of Surveying and Mapping,2016,41(3):49-53.
[6]　Tilton J C,Tarabalka Y,Montesano P,et al.Best merge region-growing segmentation with integrated nonadjacent region object aggregation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(11):4454-4467.
[7]　Beaulieu J M,Goldberg M.Hierarchy in picture segmentation:A stepwise optimization approach[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(2):150-163.
[8]　Baatz M,Schäpe A.Multiresolution segmentation:An optimizing approach for high quality multi-scale segmentation[C]//Angewandte Geographich Informationsverarbeitung,XII.Germany:Wichmann,2000:12-23.
[9]　苏腾飞,李洪玉.一种两阶段区域生长的遥感图像分割算法[J].遥感技术与应用,2015,30(3):476-485.
Su T F,Li H Y.A two stage region growing method for remote sensing image segmentation[J].Remote Sensing Technology and Application,2015,30(3):476-485.
[10] 朱俊杰,杜小平,范湘涛,等.一种改进的多尺度分形网络演化分割方法[J].遥感技术与应用,2014,29(2):324-329.
Zhu J J,Du X P,Fan X T,et al.A advanced multi-scale fractal net evolution approach[J].Remote Sensing Technology and Application,2014,29(2):324-329.
[11] Zhong Y F,Zhao B,Zhang L P.Multiagent object-based classifier for high spatial resolution imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(2):841-857.
[12] Lassalle P,Inglada J,Michel J,et al.A scalable tile-based framework for region-merging segmentation[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(10):5473-5485.
[13] Comaniciu D,Meer P.Mean shift:A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[14] Achanta R,Shaji A,Smith K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282.
[15] 孙巍,郭敏.基于SLIC与条件随机场的图像分割算法[J].计算机应用研究,2015,32(12):3817-3820,3824.
Sun W,Guo M.Image segmentation based on SLIC and conditional random field[J].Application Research of Computers,2015,32(12):3817-3820,3824.
[16] Lee H C,Cok D R.Detecting boundaries in a vector field[J].IEEE Transactions on Signal Processing,2002,39(5):1181-1194.
[17] 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.
[18] Crevier D.Image segmentation algorithm development using ground truth image data sets[J].Computer Visual and Image Understanding,2008,112(2):143-159.

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