基于全卷积网络的土壤断层扫描图像中孔隙分割
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摘要
针对土壤断层扫描图像中存在部分容积效应及因孔隙成分复杂、结构不规则等引起的分割精度低的问题,该文提出一种全卷积网络(fully convolutional network,FCN)土壤孔隙分割方法,为土壤科学研究提供技术支持。该文以黑土土壤断层扫描图像为研究对象,通过卷积和池化运算输出不同尺度的孔隙特征图;将孔隙的深层特征和浅层特征相融合,采用上采样算子对融合特征进行插值操作,从而输出孔隙的二值图。与大津法、分水岭法、区域生长法和模糊C均值聚类法(Fuzzy C-means,FCM)4种常用孔隙分割方法的对比结果表明,FCN法在低,中,高3种孔隙密度的土壤图像中优于其他4种方法。FCN法的平均分割正确率为98.1%,比4种常用方法分别高25.6%,48.3%,55.7%和9.5%;FCN法的平均过分割率和欠分割率分别为2.2%和1.3%,仅为次优方法(FCM法)的33.8%和23.6%。通过融合土壤孔隙结构的多重特征,FCN法能够实现土壤孔隙整体和局部信息的精准判断,为土壤学的研究提供了一种更加智能化的技术手段。
In this paper, a soil pore segmentation method based on fully convolutional network(FCN) is proposed to improve the accuracy of pore segmentation in soil image and provide technical support for the research of soil science. Taking the soil of typical black soil as the research object, the soil computed tomography image were obtained by scanning and cutting. Based on the FCN network, the soil image and the calibrated image of pore structure were input for convoluting, pooling and deconvoluting operations, and the error between the prediction image and the calibration image was used as feedback to complete the forward inference operation. Then, the weight value was updated by the back propagation algorithm to establish the soil pore segmentation model. Fully considering the pore geometry and spatial distribution characteristics, the pore model can accurately output the soil pore binary image. Meanwhile, the commonly used segmentation methods in the literature, such as Otsu method, watershed method, regional growth method and Fuzzy C-means method(FCM) were adopted for the comparative experiments on soil computed tomography images with low pore density(0-0.03), medium pore density(0.03-0.1) and high pore density(0.1-1) which were defined by porosity of soil. The experimental results showed that the watershed method and the regional growth method overestimate the pore structure of different geometries, including cracks between the pores, whereas the Otsu method and FCM method tended to overestimate the macropores and underestimate the micropores. Compared the five methods, the FCN method can accurately extract the pore structures with vary topologies from the complex soil computed tomography images with low, medium and high pore density. Moreover, the segmentation accuracy rate, over-segmentation rate, and under-segmentation rate were used to evaluate the soil pore segmentation performance of five methods. Based on 1487 soil computed tomography images, the average segmentation accuracy of FCN pore segmentation method was 98.1%, which was 25.6%, 48.3%, 55.7% and 9.5% higher than that of Otsu method, watershed method, regional growth method and FCM method. The average over-segmentation rate of the FCN pore segmentation method was 2.2%, which was only 33.8% of the suboptimal method(FCM method), respectively. And the average under-segmentation rate of the FCN pore segmentation method was 1.3%, which was only 23.6% of the suboptimal method(FCM method). In total, the FCN method can accurately extract the pore topology, restore the spatial distribution of pores and its application can make up for the shortcoming that the traditional segmentation method only uses the low-level features(gray and edge) when extracting the pore structure. Owing to the multiple convolution layers in the network, the FCN method can obtain the vary features of pore structure, so it has strong generalization ability and robustness of pore segmentation for different types of soil images. This paper will has a good reference for the microscopic process simulation, 3D reconstruction and soil structure analysis on the pore scale, and can provide a more intelligent technical method for soil science.
In this paper, a soil pore segmentation method based on fully convolutional network(FCN) is proposed to improve the accuracy of pore segmentation in soil image and provide technical support for the research of soil science. Taking the soil of typical black soil as the research object, the soil computed tomography image were obtained by scanning and cutting. Based on the FCN network, the soil image and the calibrated image of pore structure were input for convoluting, pooling and deconvoluting operations, and the error between the prediction image and the calibration image was used as feedback to complete the forward inference operation. Then, the weight value was updated by the back propagation algorithm to establish the soil pore segmentation model. Fully considering the pore geometry and spatial distribution characteristics, the pore model can accurately output the soil pore binary image. Meanwhile, the commonly used segmentation methods in the literature, such as Otsu method, watershed method, regional growth method and Fuzzy C-means method(FCM) were adopted for the comparative experiments on soil computed tomography images with low pore density(0-0.03), medium pore density(0.03-0.1) and high pore density(0.1-1) which were defined by porosity of soil. The experimental results showed that the watershed method and the regional growth method overestimate the pore structure of different geometries, including cracks between the pores, whereas the Otsu method and FCM method tended to overestimate the macropores and underestimate the micropores. Compared the five methods, the FCN method can accurately extract the pore structures with vary topologies from the complex soil computed tomography images with low, medium and high pore density. Moreover, the segmentation accuracy rate, over-segmentation rate, and under-segmentation rate were used to evaluate the soil pore segmentation performance of five methods. Based on 1487 soil computed tomography images, the average segmentation accuracy of FCN pore segmentation method was 98.1%, which was 25.6%, 48.3%, 55.7% and 9.5% higher than that of Otsu method, watershed method, regional growth method and FCM method. The average over-segmentation rate of the FCN pore segmentation method was 2.2%, which was only 33.8% of the suboptimal method(FCM method), respectively. And the average under-segmentation rate of the FCN pore segmentation method was 1.3%, which was only 23.6% of the suboptimal method(FCM method). In total, the FCN method can accurately extract the pore topology, restore the spatial distribution of pores and its application can make up for the shortcoming that the traditional segmentation method only uses the low-level features(gray and edge) when extracting the pore structure. Owing to the multiple convolution layers in the network, the FCN method can obtain the vary features of pore structure, so it has strong generalization ability and robustness of pore segmentation for different types of soil images. This paper will has a good reference for the microscopic process simulation, 3D reconstruction and soil structure analysis on the pore scale, and can provide a more intelligent technical method for soil science.
引文
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[24]Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition,Boston,2015:3431-3440.
[25]Dai J,He K,Sun J.Instance-aware semantic segmentation via multi-task network cascades[C]//IEEE Conference on Computer Vision and Pattern Recognition,Boston,2015:3150-3158.
[26]王恩姮,卢倩倩,陈祥伟.模拟冻融循环对黑土剖面大孔隙特征的影响[J].土壤学报,2014,51(3):490-496.Wang Enheng,Lu Qianqian,Chen Xiangwei.Characterization of macro-pores in mollisol profile subjected to simulated freezing-thawing alternation[J].Acta Pedologica Sinica,2014,51(3):490-496.(in Chinese with English abstract)
[27]赵玥,韩巧玲,赵燕东.基于灰度-梯度特征的改进FCM土壤孔隙辨识方法[J].农业机械学报,2018,49(3):279-286.Zhao Yue,Han Qiaoling,Zhao Yandong.Improved FCMmethod for pore identification based on grayscale-gradient features[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):279-286.(in Chinese with English abstract)
[28]Shen X Y,Hertzmann A,Jia J Y,et al.Automatic portrait segmentation for image stylization[J].Computer Graphics Forum,2016,35(2):93-102.
[29]Ghiasi G,Fowlkes C C.Laplacian pyramid reconstruction and refinement for semantic segmentation[C]//European Conference on Computer Vision,2016:519-534.
[30]Hapca S M,Houston A N,Otten W,te al.New local thresholding method for soil images by minimizing grayscale intra-class variance[J].Vadose Zone Journal,2013,12(3):1-13.
[2]Tokumoto I,Noborio K,Koga K.Coupled water and heat flow in a grass field with aggregated Andisol during soil-freezing periods[J].Cold Regions Science and Technology,2010,62(2/3):98-106.
[3]Yu X,Fu Y,Lu S.Characterization of the pore structure and cementing substances of soil aggregates by a combination of synchrotron radiation X-ray micro-computed tomography and scanning electron microscopy[J].European Journal of Soil Science,2017,68(1):66-79.
[4]Meira Cássaro F A,Posadas Durand A N,Gimenez D,et al.Pore-size distributions of soils derived using a geometrical approach and multiple resolution microct images[J].Soil Science Society of America Journal,2017,81(3):468-476.
[5]Taina I A,Heck R J,Elliot T R,et al.Micromorphological and X-rayμCT study of Orthic Humic Gleysols under different management conditions[J].Geoderma,2010,158(3):110-119.
[6]Cnudde V,Boone M N.High-resolution X-ray computed tomography in geosciences:A review of the current technology and applications[J].Earth-Science Reviews,2013,123:1-17.
[7]Munkholm L J,Richard H J,Deen B.Soil pore characteristics assessed from X-ray micro-CT derived images and correlations to soil friability[J].Geoderma,2012(181/182):22-29.
[8]姚志华,陈正汉,李加贵,等.基于CT技术的原状黄土细观结构动态演化特征[J].农业工程学报,2017,33(13):134-142.Yao Zhihua,Chen Zhenghan,Li Jiagui,et al.Meso-structure dynamic evolution characteristic of undisturbed loess based on CT technology[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(13):134-142.(in Chinese with English abstract)
[9]Nicholas J,Mats L,John K.Connectivity and percolation of structural pore networks in a cultivated silt loam soil quantified by X-ray tomography[J].Geoderma,2017,287:71-79.
[10]Sarah S,Erwan P,Lique L,et al.X-ray Micro-CT:How soil pore space description can be altered by image processing[J].Vadose Zone Journal,2018,17(1).DOI:10.2136/vzj2016.06.0049.
[11]Yu X,Wu C,Fu Y.Three-dimensional pore structure and carbon distribution of macroaggregates in biochar-amended soil[J].European Journal of Soil Science,2016,67(1):109-120.
[12]Jarvis N,Larsbo M,Koestel J.Connectivity and percolation of structural pore networks in a cultivated silt loam soil quantified by X-ray tomography[J].Geoderma,2017,287:71-79.
[13]Otsu N.A threshold selection method from gray-Level histograms[J].IEEE Transactions on Systems Man and Cybernetics,1979,9(1):62-66.
[14]Vincent L,Soille P.Watersheds in digital spaces:An efficient algorithm based on immersion simulations[J].IEEETransactions on Pattern Analysis&Machine Intelligence,1991,13(6):583-598.
[15]Zhou H,Mooney S J,Peng X H.Bimodal soil pore structure investigated by a combined soil water retention curve and X-ray Computed Tomography approach[J].Soil Science Society of America Journal,2017,81(6):1270-1278.
[16]Kamdi S,Krishna R K.Image segmentation and region growing algorithm[J].International Journal of Computer Technology&Electronics Engineering,2012,1(2):103-107.
[17]Schnaar G,Brusseau M L.Characterizing pore-scale configuration of organic immiscible liquid in multiphase systems with synchrotron X-ray microtomography[J].Vadose Zone Journal,2006,5(2):641-648.
[18]McBratney A B,Odeh I O A.Application of fuzzy sets in soil science:Fuzzy logic,fuzzy measurements and fuzzy decisions[J].Geoderma,1997,77(2/3/4):85-113.
[19]Sun X L,Zhao Y G,Wang H L,et al.Sensitivity of digital soil maps based on FCM to the fuzzy exponent and the number of clusters[J].Geoderma,2012(171/172):24-34.
[20]赵玥,韩巧玲,赵燕东.基于CT扫描技术的土壤孔隙定量表达优化[J].农业机械学报,2017,48(10):252-259.Zhao Yue,Han Qiaoling,Zhao Yandong.Optimization of soil pore quantitative expression based on computed tomography scanning technology[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(10):252-259.(in Chinese with English abstract)
[21]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Neural Information Processing Systems,Nevada,2012:1097-1105.
[22]Noh H,Hong S,Han B.Learning deconvolution network for semantic segmentation[C]//IEEE International Conference on Computer Vision,Liverpool,2015:1520-1528.
[23]Hariharan B,Arbeláez P,Girshick R,et al.Simultaneous detection and segmentation[C]//European Conference on Computer Vision,Zürich,2014:297-312.
[24]Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition,Boston,2015:3431-3440.
[25]Dai J,He K,Sun J.Instance-aware semantic segmentation via multi-task network cascades[C]//IEEE Conference on Computer Vision and Pattern Recognition,Boston,2015:3150-3158.
[26]王恩姮,卢倩倩,陈祥伟.模拟冻融循环对黑土剖面大孔隙特征的影响[J].土壤学报,2014,51(3):490-496.Wang Enheng,Lu Qianqian,Chen Xiangwei.Characterization of macro-pores in mollisol profile subjected to simulated freezing-thawing alternation[J].Acta Pedologica Sinica,2014,51(3):490-496.(in Chinese with English abstract)
[27]赵玥,韩巧玲,赵燕东.基于灰度-梯度特征的改进FCM土壤孔隙辨识方法[J].农业机械学报,2018,49(3):279-286.Zhao Yue,Han Qiaoling,Zhao Yandong.Improved FCMmethod for pore identification based on grayscale-gradient features[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):279-286.(in Chinese with English abstract)
[28]Shen X Y,Hertzmann A,Jia J Y,et al.Automatic portrait segmentation for image stylization[J].Computer Graphics Forum,2016,35(2):93-102.
[29]Ghiasi G,Fowlkes C C.Laplacian pyramid reconstruction and refinement for semantic segmentation[C]//European Conference on Computer Vision,2016:519-534.
[30]Hapca S M,Houston A N,Otten W,te al.New local thresholding method for soil images by minimizing grayscale intra-class variance[J].Vadose Zone Journal,2013,12(3):1-13.