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

Abstract

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

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.DOI:10.12082/dqxxkx.2019.180519

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 1

Fig. 5

Fig. 6

Tab. 2

Tab. 3

Tab. 4

Fig. 7

Tab. 5

Fig. 8

Tab. 6

Tab. 7

References 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

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

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 15

References 20

Related Articles 15

Metrics

Comments

Recommended 0

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]	CHEN Ang, YANG Xiuchun, XU Bin, JIN Yunxiang, ZHANG Wenbo, GUO Jian, XING Xiaoyu, YANG Dong. Research on Recognition Methods of Elm Sparse Forest based on Object-based Image Analysis and Deep Learning [J]. Journal of Geo-information Science, 2020, 22(9): 1897-1909.
[2]	CUI Cheng, REN Hongyan, ZHAO Lu, ZHUANG Dafang. Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery [J]. Journal of Geo-information Science, 2020, 22(6): 1330-1338.
[3]	MAO Wenshan, ZHAO Hongli, SUN Fengjiao, JIANG Yunzhong, JIANG Qian, ZHU Yanru. Personalized Recommendation Method of Thematic Map Products based on Item2Vec with Negative Sampling Optimization [J]. Journal of Geo-information Science, 2020, 22(11): 2128-2139.
[4]	HE Hongshu, HUANG Xiaoxia, LI Hongga, NI Lingjia, WANG Xinge, CHEN Chong, LIU Ze. Water Body Extraction of High Resolution Remote Sensing Image based on Improved U-Net Network [J]. Journal of Geo-information Science, 2020, 22(10): 2010-2022.
[5]	GUO Zihui, LIU Wei. Land Type Interpretation Authenticity Check of Vector Patch Supported by Deep Learning and Remote Sensing Image [J]. Journal of Geo-information Science, 2020, 22(10): 2051-2061.
[6]	SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia. A Tentative Study on System of Software Technology for Artificial Intelligence GIS [J]. Journal of Geo-information Science, 2020, 22(1): 76-87.
[7]	CAI Bowen,WANG Shugen,WANG Lei,SHAO Zhenfeng. Extraction of Urban Impervious Surface from High-Resolution Remote Sensing Imagery based on Deep Learning [J]. Journal of Geo-information Science, 2019, 21(9): 1420-1429.
[8]	CHENG Bo, LI Weihong, TONG Haoxin. Chinese Address Segmentation based on BiLSTM-CRF [J]. Journal of Geo-information Science, 2019, 21(8): 1143-1151.
[9]	Huihui CAI, Wei ZHU, Zixuan LI, Yuanyuan LIU, Longbin LI, Chang LIU. Prediction Method of Tungsten-molybdenum Prospecting Target Area based on Deep Learning [J]. Journal of Geo-information Science, 2019, 21(6): 928-936.
[10]	Ying FANG, Lianfa LI. Spatiotemporal Estimation of High-Accuracy and High-Resolution Meteorological Parameters based on Machine Learning [J]. Journal of Geo-information Science, 2019, 21(6): 799-813.
[11]	Gang ZHANG, Wenbin LIU, Nan ZHANG. Progressive Morphological Filtering Method of Dense Matching Point Cloud based on Region Feature Segmentation [J]. Journal of Geo-information Science, 2019, 21(4): 615-622.
[12]	Yao YAO, Shuliang REN, Junyi WANG, Qingfeng GUAN. Mapping the Fine-Scale Housing Price Distribution by Integrating a Convolutional Neural Network and Random Forest [J]. Journal of Geo-information Science, 2019, 21(2): 168-177.
[13]	LIU Hao, LUO Jiancheng, HUANG Bo, YANG Haiping, HU Xiaodong, XU Nan, XIA Liegang. Building Extraction based on SE-Unet [J]. Journal of Geo-information Science, 2019, 21(11): 1779-1789.
[14]	YANG Jinyi,XU Weiming,WANG Chengjun,WENG Qian. High-Resolution Remote Sensing Imagery Change Detection based on Super-Pixel BOW Features and Active Learning [J]. Journal of Geo-information Science, 2019, 21(10): 1594-1607.
[15]	Liying ZHANG, Tao PEI, Yijin CHEN, Ci SONG, Xiaoqian LIU. A Review of Urban Environmental Assessment based on Street View Images [J]. Journal of Geo-information Science, 2019, 21(1): 46-58.

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