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纸质出版日期： 2020-12-07 ，
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洪亮，楚森森，彭双云，许泉立.2020.顾及全局和局部最优的高分辨率遥感影像多尺度分割优化算法.遥感学报，24（12）： 1464-1475
Hong L，Chu S S，Peng S Y and Xu Q L. 2020. Multiscale segmentation-optimized algorithm for high-spatial remote sensing imagery considering global and local optimizations. Journal of Remote Sensing（Chinese）， 24（12）：1464-1475
遥感影像多尺度分割是面向对象影像分析方法（OBIA）的关键步骤，分割质量直接影响OBIA的分类精度，目前多尺度分割方法都很难让分割结果同时达到全局和局部最优。本文针对上述问题，提出一种新的顾及全局和局部最优的高分辨率遥感影像多尺度分割优化算法。该算法主要包括：（1）采用局部方差准则获得多尺度分割的全局最优分割尺度。（2）对全局最优分割尺度中的过分割和欠分割对象进行优化处理，获得局部最优分割结果。（3）将局部最优化分割结果与全局最优分割结果进行融合，获得最终的多尺度优化分割结果。本文采用2个QuickBird遥感影像进行实验，验证该算法的有效性，并对实验结果进行定性和定量分析，结果表明：（1）从视觉效果来看，优化后的分割结果具有更准确的分割边界，大尺度的地物保持较好的区域性，小尺度的地物保持了更多细节。（2）从定量评价指标（RR、RI和ARI）分析：在实验1中，该算法比全局最优分割尺度的RR\RI\ARI分别提高了2.1%，2.4%，30.2%，比基于K均值优化算法分别提高了8.3%，0.1%，8.1%，比融合边界优化算法分别提高了0.7%，0.4%，17.6%；在实验2中，该算法比全局最优分割尺度的RR\RI\ARI分别提高了4.5%，2.7%，29.3%，比基于K均值优化算法分别提高了17%，0.8%，8.4%，比融合边界优化算法分别提高了1.7%，2.5%，17.2%。（3）相对典型分割算法，该算法的优化结果达到了局部和全局最优；相对其他多尺度分割优化算法，该算法同时减少了欠分割和过分割对象。
With the significant improving for the spatial resolution of remote sensing imagery
the limitation of the traditional pixel-based methods for medium and low resolution remote sensing image have become obvious. In recent decades
the Object Based Image Analysis (OBIA) has become the most popular information extraction method for the high spatial resolution remote sensing imagery. The object is the based processing units in the OBIA method
so that the segmentation method obtaining the objects is a key step in the OBIA
because the classification accuracy is directly affected by the quality of segmentation results. However
the objects in high-resolution remote sensing images show multi-scale characteristics
and it is difficult to accurately obtain the optimal segmentation results using the single segmentation scale
so that he multi-scale segmentation method have become an inevitable choice. But the multi-scale segmentation algorithms proposed in previous literatures are difficult to achieve global and local optimization. In this paper
a new multiscale segmentation optimized algorithm was proposed. The algorithm mainly includes following steps: (1) the global optimal scale in multiscale segmentation was obtained for using the local variance criterion; (2) the over-segmentation and under-segmentation objects in global optimized scale were respectively optimized to obtain the local optimization results; (3) the global and local optimized results were fused to obtain the finishing optimized results. In this paper
the two high spatial resolution remote sensing images which respectively located in Dongguan
China and Florida
USA were used to verify the effectiveness of the proposed algorithm
and the experimental results were analyzed by the qualitative and quantitative evaluation .The results were shown following as: (1) From the perspective of visual effects
the more accurate segmentation boundary were obtained in the optimized results
and the objects (such as road
farmland
and water) in the large scale maintain better regional feature
and the objects (such as tree
house
shadow)in the small scale have more detail information. (2) From the perspective of quantitative analysis by the evaluation indicators(RR
RI and ARI)
the presented algorithm increased the RR
RI and ARI by 2.1%
2.4 and 30.2% in comparison with the global optimized segmentation scale
and by 8.3%，0.1% and 8.1% in comparison with the k-means optimized method
and by 0.7%，0.4% and 17.6% in comparison with the fused boundary optimized method in the test 1
and increased the RR
RI and ARI by 4.5%，2.7% and 29.3% in comparison with the global optimized segmentation scale
and by 17%，0.8% and 8.4% in comparison with the k-means optimized method
and by 1.7%，2.5% and 17.2% in comparison with the fused boundary optimized method in the test 2. In summary
compared with classical segmentation algorithms
the proposed algorithm obtained the best segmentation results by both local and global optimization
and reduced over-segmentation and under-segmentation objects in the segmentation results. Meanwhile
the heterogeneity of objects is different in the different types of scenes
for example
the objects in the city scenes are high heterogeneity
but the objects of the rural scenes are high homogeneity. So that
the optimal segmentation parameters in multi-scale segmentation is difficult to other scenes.
遥感高分辨率遥感影像多尺度分割局部莫兰指数空间统计指数优化算法
remote sensinghigh spatial resolution remote sensing imagerymulti-scale segmentationLocal Moran’s Ispatial statistic indexoptimized algorithm
Anselin L. 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27(2): 93-115 [DOI: 10.1111/j.1538-4632.1995.tb00338.xhttp://dx.doi.org/10.1111/j.1538-4632.1995.tb00338.x]
Benz U C, Hofmann P, Willhauck G, Lingenfelder I and Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3/4): 239-258 [DOI: 10.1016/j.isprsjprs.2003.10.002http://dx.doi.org/10.1016/j.isprsjprs.2003.10.002]
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16 [DOI: 10.1016/j.isprsjprs.2009.06.004http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004]
Blaschke T, Hay G J, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R Q, van der Meer F, van der Werff H, van Coillie F and Tiede D. 2014. Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 180-191 [DOI: 10.1016/j.isprsjprs.2013.09.014http://dx.doi.org/10.1016/j.isprsjprs.2013.09.014]
Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6): 679-698 [DOI: 10.1109/TPAMI.1986.4767851http://dx.doi.org/10.1109/TPAMI.1986.4767851]
Carleer A P, Debeir O and Wolff E. 2005. Assessment of very high spatial resolution satellite image segmentations. Photogrammetric Engineering and Remote Sensing, 71(11): 1285-1294 [DOI: 10.14358/PERS.71.11.1285http://dx.doi.org/10.14358/PERS.71.11.1285]
Chen J, Deng M, Mei X M, Chen T Q, Shao Q B and Hong L. 2014. Optimal segmentation of a high-resolution remote-sensing image guided by area and boundary. International Journal of Remote Sensing, 35(19): 6914-6939 [DOI: 10.1080/01431161.2014.960617http://dx.doi.org/10.1080/01431161.2014.960617]
Chen Z, Zhao Z, Gong P and Zeng B. 2006. A new process for the segmentation of high resolution remote sensing imagery. International Journal of Remote Sensing, 27(22): 4991-5001 [DOI: 10.1080/01431160600658131http://dx.doi.org/10.1080/01431160600658131]
Chu S S, Hong L, Chen J, Deng M, Yang K and Liu C. 2016. Segmentation optimization algorithm based on the fusion information of the boundaries of a high-resolution remote sensing image. Journal of Image and Graphics, 21(8): 1096-1105
楚森森, 洪亮, 陈杰, 邓敏, 杨昆, 刘纯. 2016. 融合边界信息的高分辨率遥感影像分割优化算法. 中国图象图形学报, 21(8): 1096-1105 [DOI: 10.11834/jig.20160814http://dx.doi.org/10.11834/jig.20160814]
Csillik O. 2017. Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sensing, 9(3): 243 [DOI: 10.3390/rs9030243http://dx.doi.org/10.3390/rs9030243]
Drăguţ L, Csillik O, Eisank C and Tiede D. 2014. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88: 119-127 [DOI: 10.1016/j.isprsjprs.2013.11.018http://dx.doi.org/10.1016/j.isprsjprs.2013.11.018]
Drǎguţ L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871 [DOI: 10.1080/13658810903174803http://dx.doi.org/10.1080/13658810903174803]
Hu X Y, Tao C V and Prenzel B. 2005. Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity, and color features. Photogrammetric Engineering and Remote Sensing, 71(12): 1399-1406 [DOI: 10.14358/PERS.71.12.1399http://dx.doi.org/10.14358/PERS.71.12.1399]
Li P and Xiao X. 2007. Multispectral image segmentation by a multichannel watershed‐based approach. International Journal of Remote Sensing, 28(19): 4429-4452 [DOI: 10.1080/014311606010 34910http://dx.doi.org/10.1080/01431160601034910]
Li X F, Zhang S Q, Liu Q, Zhang B, Liu D W, Lu B B and Na X D. 2009. Fast segmentation method of high-resolution remote sensing image. Journal of Infrared and Millimeter Waves, 28(2): 146-150
李晓峰, 张树清, 刘强, 张柏, 刘殿伟, 卢碧波, 那晓东. 2009. 高分辨率遥感影像的快速分割方法. 红外与毫米波学报, 28(2): 146-150 [DOI: 10.3321/j.issn:1001-9014.2009.02.016http://dx.doi.org/10.3321/j.issn:1001-9014.2009.02.016]
Liu D S and Xia F. 2010. Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4): 187-194 [DOI: 10.1080/01431161003743173http://dx.doi.org/10.1080/01431161003743173]
Ma L, Li M C, Ma X X, Cheng L, Du P J and Liu Y X. 2017. A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 277-293 [DOI: 10.1016/j.isprsjprs.2017.06.001http://dx.doi.org/10.1016/j.isprsjprs.2017.06.001]
Ma Y N, Ming D P and Yang H P. 2017. Scale estimation of object-oriented image analysis based on spectral-spatial statistics. Journal of Remote Sensing, 21(4): 566-578
马燕妮, 明冬萍, 杨海平. 2017. 面向对象影像多尺度分割最大异质性参数估计. 遥感学报, 21(4): 566-578 [DOI: 10.11834/jrs.20176374http://dx.doi.org/10.11834/jrs.20176374]
Ming D P, Li J, Wang J Y and Zhang M. 2015. Scale parameter selection by spatial statistics for GeOBIA: using mean-shift based multi-scale segmentation as an example. ISPRS Journal of Photogrammetry and Remote Sensing, 106: 28-41 [DOI: 10.1016/j.isprsjprs.2015.04.010http://dx.doi.org/10.1016/j.isprsjprs.2015.04.010]
Wang H X, Jin H J, Wang J L and Jiang W S. 2015. Optimization approach for multi-scale segmentation of remotely sensed imagery under k-means clustering guidance. Acta Geodaetica et Cartographica Sinica, 44(5): 526-532
王慧贤, 靳惠佳, 王娇龙, 江万寿. 2015. k均值聚类引导的遥感影像多尺度分割优化方法. 测绘学报, 44(5): 526-532 [DOI: 10.11947/j.AGCS.2015.20130497http://dx.doi.org/10.11947/j.AGCS.2015.20130497]
Wang L G, Dai Q L, Xu Q Z and Zhang Y. 2015. Constructing hierarchical segmentation tree for feature extraction and land cover classification of high resolution MS imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): 1946-1961 [DOI: 10.1109/JSTARS.2015.2428232http://dx.doi.org/10.1109/JSTARS.2015.2428232]
Wang L G, Liu G Y and Dai Q L. 2014. Optimization of segmentation algorithms through mean-shift filtering preprocessing. IEEE Geoscience and Remote Sensing Letters, 11(3): 622-626 [DOI: 10.1109/LGRS.2013.2272574http://dx.doi.org/10.1109/LGRS.2013.2272574]
Wuest B and Zhang Y. 2009. Region based segmentation of QuickBird multispectral imagery through band ratios and fuzzy comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1): 55-64 [DOI: 10.1016/j.isprsjprs.2008.06.005http://dx.doi.org/10.1016/j.isprsjprs.2008.06.005]
Xiao P F, Zhang X L, Zhang H M, Hu R and Feng X Z. 2018. Multiscale optimized segmentation of urban green cover in high resolution remote sensing image. Remote Sensing, 10(11): 1813 [DOI: 10.3390/rs10111813http://dx.doi.org/10.3390/rs10111813]
Yang J, He Y H and Weng Q H. 2015. An automated method to parameterize segmentation scale by enhancing intrasegment homogeneity and intersegment heterogeneity. IEEE Geoscience and Remote Sensing Letters, 12(6): 1282-1286 [DOI: 10.1109/LGRS.2015.2393255http://dx.doi.org/10.1109/LGRS.2015.2393255]
Zhang X L, Feng X Z and Xiao P F. 2015. Multi-scale segmentation of high-spatial resolution remote sensing images using adaptively increased scale parameter. Photogrammetric Engineering and Remote Sensing, 81(6): 461-470 [DOI: 10.14358/PERS.81.6.461http://dx.doi.org/10.14358/PERS.81.6.461]
Zhang X L, Xiao P F, Feng X Z, Wang J G and Wang Z. 2014. Hybrid region merging method for segmentation of high-resolution remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 98: 19-28 [DOI: 10.1016/j.isprsjprs.2014.09.011http://dx.doi.org/10.1016/j.isprsjprs.2014.09.011]
Zhang X L, Xiao P F, Song X Q and She J F. 2013. Boundary-constrained multi-scale segmentation method for remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 78: 15-25 [DOI: 10.1016/j.isprsjprs.2013.01.002http://dx.doi.org/10.1016/j.isprsjprs.2013.01.002]
Zhao B, Zhong Y F and Zhang L P. 2013. High spatial resolution remote sensing image segmentation based on multi-agent theory. Acta Geodaetica et Cartographica Sinica, 42(1): 108-115, 122
赵贝, 钟燕飞, 张良培. 2013. 高空间分辨率遥感影像的多智能体分割方法研究. 测绘学报, 42(1): 108-115, 122
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