基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物

doi:10.12082/dqxxkx.2019.180519

地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (2): 259-268.doi: 10.12082/dqxxkx.2019.180519

基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物

刘懿兰^1,²(), 黄晓霞^1,^*(), 李红旮¹, 柳泽³, 陈崇³, 王新歌³

1. 中国科学院遥感与数字地球研究所,北京 100101
2. 中国科学院大学,北京100049
3. 住房和城乡建设部城乡规划管理中心,北京 100835

收稿日期:2018-10-17 修回日期:2018-12-03 出版日期:2019-02-20 发布日期:2019-01-30
通讯作者: 黄晓霞 E-mail:18649052480@163.com;hxx@irsa.ac.cn
作者简介:
作者简介：刘懿兰（1994-）,女,天津人,硕士,研究方向为深度学习,地理信息系统等。E-mail: 18649052480@163.com
基金资助:
国家重点研发计划（2017YFB0503905）;国土资源部城市土地资源监测与仿真重点实验室开放课题（KF-2016-02-012、KF-2018-03-032）

Extraction of Irregular Solid Waste in Rural based on Convolutional Neural Network and Conditional Random Field Method

Yilan LIU^1,²(), Xiaoxia HUANG^1,^*(), Hongga LI¹, Ze LIU³, Chong CHENG³, Xin'ge WANG³

1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Ministry of Housing and Urban-Rural Development of the People's Republic of China, Beijing 100835, China

Received:2018-10-17 Revised:2018-12-03 Online:2019-02-20 Published:2019-01-30
Contact: Xiaoxia HUANG E-mail:18649052480@163.com;hxx@irsa.ac.cn
Supported by:
National Key Research and Development Program of China, No.2017YFB0503905;The Project Supported by the Open of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Land and Resources, No.KF-2016-02-012, KF-2018-03-032

摘要/Abstract

摘要：

随着村镇经济建设发展,生活垃圾和工业固体废弃物造成的污染问题日益突出,已经成为制约新农村建设发展和生态文明建设的关键问题,而目前针对乡镇非正规固体废弃物的调查与统计主要依赖全国各乡镇相关部门逐级调查上报,工作量较大。本文基于高分辨率遥感影像,将深度学习模型和条件随机场模型相结合引入到乡镇固体废弃物的提取研究中,探索一种基于深度卷积神经网络的乡镇固体废弃物提取模型。由于固体废弃物在影像上表现为面积小,分布破碎等特点,为了提高工作效率,将模型特分为识别和提取2个部分：① 通过全连接卷积网络（CNN）对固体废弃物进行快速识别判断,筛选感兴趣区域影像块;② 在传统的全卷积神经网络(FCN)的基础上加入条件随机场模型(CRF)提取固体废弃物边界,提高整体分割精度。根据安徽、山西等地区相关部门上报固体废弃物堆放点以及住房与城乡建设部城乡规划管理中心进行野外检查的结果,实验最终识别精度达到86.87%以上;形状提取精度为89.84%,Kappa系数为0.7851,识别与提取精度均优于传统分类方法。同时,该方法已经逐步应用于住房和城乡建设部有关成都、兰州、河北等部分乡镇非正规固体废弃物的核查工作,取得了较为满意的结果。

关键词: 高分辨率遥感影像, 深度学习, 固体废弃物, 卷积神经网络, 条件随机场

Abstract:

With the development of rural economic construction, the pollution problem caused by domestic waste and industrial solid waste has become increasingly prominent, which has become a key problem to restrict the construction of the new rural developing and ecological civilization. At present, the investigation and statistics of informal solid waste in rural areas mainly depend on the reports of departments of each township step by step, and the workload is large. So based on high-resolution remote sensing images, this paper combines Deep Learning model with Conditional Random Field model to the study of rural solid waste extracting, and explores a recognition and extraction model of rural solid waste based on Deep Convolution Neural Network. Due to the solid waste in images is characterized by small size, distribution ,fragmentation and so on, in order to improve the efficiency, the model is divided into two parts: Recognition and Extraction. In the first part, a Full-connected Convolution Network (CNN) is used to identify and judge solid wastes quickly, and the image blocks include the interesting regions are screened. In the second part, Conditional Random Field model (CRF) is added to the traditional Full Convolution Neural Network (FCN) to extract boundary of solid waste and improve the overall segmentation accuracy.According to the relevant reports about solid waste of some rural areas in Anhui and Shanxi province and the field inspection by the urban and rural planning and management center of the Ministry of Housing and Urban-Rural Construction, Compared with the test results of the model in this paper,the results show the recognition accuracy is 86.87%,the shape extraction accuracy is 89.84%,and the Kappa coefficient is 0.7851. So the recognition and extraction accuracy of the paper's method is proved to be superior to the traditional methods. At the same time, this method has been gradually applied to the investigation of informal solid waste in countryside in Chengdu, Lanzhou, Hebei and other provinces, and achieved satisfactory results.

Key words: High-resolution remote sensing image, deep Learning, solid waste, convolution neural network, conditional random field

刘懿兰, 黄晓霞, 李红旮, 柳泽, 陈崇, 王新歌. 基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物[J]. 地球信息科学学报, 2019, 21(2): 259-268.

Yilan LIU, Xiaoxia HUANG, Hongga LI, Ze LIU, Chong CHENG, Xin'ge WANG. Extraction of Irregular Solid Waste in Rural based on Convolutional Neural Network and Conditional Random Field Method[J]. Journal of Geo-information Science, 2019, 21(2): 259-268.

图/表 15

图1

图2

图3

图4

表1

图5

图6

表2

表3

表4

图7

表5

图8

表6

表7

参考文献 20

[1]	Bagheri S, Hordon R M.Hazardous waste site identification using aerial photography: A pilot study in Burlington County, New Jersey, USA[J]. Environmental Management,1988,12(3):411-412. doi: 10.1007/BF01867383
[2]	Biotto G, Silvestri S, Gobbo L, et al.GIS, multi-criteria and multi-factor spatial analysis for the probability assessment of the existence of illegal landfills[J]. International Journal of Geographical Information Science, 2009,23(10):1233-1244. doi: 10.1080/13658810802112128
[3]	刘庆生,刘高焕.基于CBERS CCD图像的焦家坡垃圾填埋场动态变化监测[J].环境卫生工程,2010,18(5):17-19. doi: 10.3969/j.issn.1005-8206.2010.05.007
	[ Liu Q S, Liu G H.Using CBERS CCD images to monitor dynamic change of Jiaojiapo waste landfill site[J]. Environmental Sanitation Engineering, 2010,18(5):17-19. ] doi: 10.3969/j.issn.1005-8206.2010.05.007
[4]	刘亚岚,任玉环,魏成阶,等.北京1号小卫星监测非正规垃圾场的应用研究[J].遥感学报,2009,13(2):320-326. doi: 10.3321/j.issn:1007-4619.2009.02.021
	[ Liu Y L, Ren Y H, Wei C J, et al.Application research of Beijing 1 small satellite monitoring irregular garbage dump[J]. Journal of Remote Sensing,2009,13(2):320-326. ] doi: 10.3321/j.issn:1007-4619.2009.02.021
[5]	张方利,杜世宏,郭舟.应用高分辨率影像的城市固体废弃物提取[J].光谱学与光谱分析,2013,33(8):2024-2030.
	[ Zhang F L, Du S H, Guo Z.Extraction of municipal solid waste from high resolution images[J]. Spectroscopy and Spectral Analysis, 2013,33(8):2024-2030. ]
[6]	雒立群,郭舟,赵文智,等.结合高光谱和高空间分辨率影像提取城市固体废弃物堆[J].测绘通报,2016(2):38-41. doi: 10.13474/j.cnki.11-2246.2016.0044
	[ Luo L Q, Guo Z, Zhao W Z, et al.Combining hyperspectral and high-resolution images to extract municipal solid waste dumps[J]. Bulletin of Surveying and Mapping,2016(2):38-41. ] doi: 10.13474/j.cnki.11-2246.2016.0044
[7]	Hinton G E, Salakhutdinov R R.Supporting online material for "Reducing the dimensionality of data with Neural Networks"[J]. Science, 2006,313(5786):504-507. doi: 10.1126/science.1127647
[8]	Lecun Y, Bengio Y.The handbook of brain theory and neural networks: Convolutional networks for images, speech, and time series[M]. Cambridge, Massachusetts: the MIT Press, 1998:255-258.
[9]	Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with Deep Convolutional Neural Networks[J].Advances in Neural Information Processing Systems(NIPS),2012(25):1097-1105. doi: 10.1145/3065386
[10]	Sermanet P, Eigen D, Zhang X, et al.OverFeat: Integrated recognition, localization and detection using Convolutional Networks[C]. International Conference on Learning Representations (ICLR), 2014:1-16.
[11]	Simonyan K, Zisserman A.Very deep convolutional networks for large-scale image recognition[C]. International Conference on Learning Representations (ICLR),2015:1-14.
[12]	Hinton G E, Osindero S, The Y W.A Fast learning algorithm for Deep Belief Nets[J]. Neural Computation, 2006,18(7):1527-54. doi: 10.1162/neco.2006.18.7.1527
[13]	付征叶,郑逢斌,刘扬.高分辨率遥感影像目标分类与识别研究进展[J].地球信息科学学报,2015,17(9):1080-1091. doi: 10.3724/SP.J.1047.2015.01080
	[ Fu Z Y, Zheng F B, Liu Y.Review on high resolution remote sensing image classification and recognition[J]. Journal of Geo-information Science, 2015,17(9):1080-1091. ] doi: 10.3724/SP.J.1047.2015.01080
[14]	党宇,张继贤,邓喀中,等.基于深度学习AlexNet的遥感影像地表覆盖分类评价研究[J].地球信息科学学报,2017,19(11):1530-1537.
	[ Dang Y, Zhang J X, Deng K Z, et al.Study on the evaluation of land cover classification using remote sensing images based on AlexNet[J]. Journal of Geo-information Science, 2017,19(11):1530-1537. ]
[15]	Vedaldi A, Lenc K. MatConvNet-Manual: Convolutional Neural Networks for MATLAB[EB/OL]. , 2015:689-692.
[16]	Long J, Shelhamer E, Darrell T.Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017,39(4):640-651. doi: 10.1109/TPAMI.2016.2572683 pmid: 27244717
[17]	Zheng S, Jayasumana S, Romera-Paredes B, et al.Conditional Random Fields as Recurrent Neural Networks[C]. IEEE International Conference on Computer Vision. IEEE, 2016:1529-1537.
[18]	Zhao J, Zhong Y, Shu H, et al.High-resolution image classification integrating spectral-spatial-location cues by conditional random fields[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2016,25(9):4033-4045. doi: 10.1109/TIP.2016.2577886 pmid: 27295673
[19]	Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with Deep Convolutional Nets and Fully Connected CRFs[J]. Computer Science,2015(4):357-361. doi: 10.1080/17476938708814211
[20]	Krähenbühl P, Koltun V. Efficient inference in fully connected CRFs with Gaussian edge potentials[J].Advances in Neural Information Processing Systems(NIPS), 2011(24):109-117.

样本集	土地利用类型	数量
背景	多层建筑物	584
	低矮建筑物	700
	农田	500
	植被	500
	道路	500
	水体	500
	裸（沙）地	500
	停车场	190
	农业大棚	83
	其他地物	215
固体废弃物	生活垃圾建筑垃圾	257

参数	数值
迭代次数	100
批量大小	100
学习率	0.0005

参数	数值
迭代次数	300
批量大小	10
学习率	0.0005

参数		数值
迭代次数		10
高斯核权重	一阶势函数权重	1 7 15
	高斯核权重1
	高斯核权重2
像素位置及相似度调节参数	参数1	8 13 6
	参数2
	参数3

测试样本类型	总数量	正确识别数/个	错误识别数/个	识别精度/%
训练集	3624	3502	122	96.64
测试集	905	786	119	86.87

基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物

Extraction of Irregular Solid Waste in Rural based on Convolutional Neural Network and Conditional Random Field Method

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 20

相关文章 15

Metrics

本文评价

推荐阅读 10

平均精度	PA/%	MA/%	Kappa系数
SVM	81.55	81.17	0.6184
FCN	84.81	83.05	0.6735
FCN+CRF	89.84	88.51	0.7851

[1]	刘淑涵, 王艳东, 付小康. 利用卷积神经网络提取微博中的暴雨灾害信息[J]. 地球信息科学学报, 2019, 21(7): 1009-1017.
[2]	蔡惠慧, 朱伟, 李孜轩, 刘园园, 李龙斌, 刘畅. 基于深度学习的钨钼找矿靶区预测方法研究[J]. 地球信息科学学报, 2019, 21(6): 928-936.
[3]	方颖, 李连发. 基于机器学习的高精度高分辨率气象因子时空估计[J]. 地球信息科学学报, 2019, 21(6): 799-813.
[4]	张刚, 刘文彬, 张男. 基于区域特征分割的密集匹配点云渐进形态学滤波[J]. 地球信息科学学报, 2019, 21(4): 615-622.
[5]	姚尧, 任书良, 王君毅, 关庆锋. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 2019, 21(2): 168-177.
[6]	张丽英, 裴韬, 陈宜金, 宋辞, 刘小茜. 基于街景图像的城市环境评价研究综述[J]. 地球信息科学学报, 2019, 21(1): 46-58.
[7]	罗星,徐伟铭,王佳. 基于对象BOW特征的高分辨率遥感影像变化检测方法[J]. 地球信息科学学报, 2018, 20(8): 1150-1159.
[8]	王成军,毛政元,徐伟铭,翁谦. 超像素与主动学习相结合的遥感影像变化检测方法[J]. 地球信息科学学报, 2018, 20(2): 235-245.
[9]	刘文涛,李世华,覃驭楚. 基于全卷积神经网络的建筑物屋顶自动提取[J]. 地球信息科学学报, 2018, 20(11): 1562-1570.
[10]	王金传,谭喜成,王召海,钟燕飞,董华萍,周松涛,成布怡. 基于Faster R-CNN深度网络的遥感影像目标识别方法研究[J]. 地球信息科学学报, 2018, 20(10): 1500-1508.
[11]	陈工,李琦,金玲艳,梁贺明,莫玉琴. 基于深度学习的区域生态安全时空模拟与预测[J]. 地球信息科学学报, 2017, 19(7): 915-923.
[12]	曲畅,任玉环,刘亚岚,李娅. POI辅助下的高分辨率遥感影像城市建筑物功能分类研究[J]. 地球信息科学学报, 2017, 19(6): 831-837.
[13]	王万国,田兵,刘越,刘俍,李建祥. 基于RCNN的无人机巡检图像电力小部件识别研究[J]. 地球信息科学学报, 2017, 19(2): 256-263.
[14]	党宇,张继贤,邓喀中,赵有松,余凡. 基于深度学习AlexNet的遥感影像地表覆盖分类评价研究[J]. 地球信息科学学报, 2017, 19(11): 1530-1537.
[15]	苏腾飞,张圣微,李洪玉. 基于优化合并的高分辨率遥感影像分割算法[J]. 地球信息科学学报, 2016, 18(7): 931-940.

序号	SVM提取结果	FCN提取结果	FCN+CRF提取结果
示例1
示例2
示例3