|
|
|
|
|
|
MaxEnt-based multi-class classification of land use in remote sensing image interpretation |
XIONG Dongyang1,2,3(), ZHANG Lin2,4, LI Guoqing2,4() |
1. The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling 712100, China 2. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China 3. University of Chinese Academy of Sciences, Beijing 100049, China 4. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China |
|
|
Abstract The one-class classification (OCC) of land use in image interpretation is a hot research topic of remote sensing. Many novel algorithms of OCC were introduced and developed. The maximum entropy model (MaxEnt)-the most promising OCC algorithm as evaluated-is widely used in the OCC study of land use. However, it is unclear about the applicability of these algorithms (including MaxEnt) in multi-class classification (MCC) of land use. Thus, this study established a procedure for MaxEnt-based land-use MCC in remote sensing image interpretation and applied the procedure to the land-use MCC of the Yunyan River basin. The overall classification effect of MaxEnt and the performance of MaxEnt in the prediction of various land were evaluated using overall classification accuracy, Kappa coefficient, sensitivity, and specificity. Moreover, the Kappa coefficient was also used to evaluate the consistency between MaxEnt and random forest (RF), maximum likelihood classification (MLC), and support vector machine (SVM) in the prediction of land use maps. The results are as follows: ① MaxEnt showed the best classification effect, with overall classification accuracy of 84% and a Kappa coefficient of 0.8; ② MaxEnt showed no worst performance in any land type, and even performed the best in some land types; ③ MaxEnt showed high classification consistency with RF and SVM, and the consistency evaluation of the land use maps obtained using the three algorithms yielded Kappa coefficients of greater than 0.6; ④ Compared with the other the three algorithms, MLC yielded a significantly different land use map, with a Kappa coefficient of less than 0.4. This result indicates that MLC is not applicable to the interpretation of land use of the study area. The procedure established in this study only depends on the occurrence probability of land use rather than the threshold selected. As a result, the OCC algorithms represented by MaxEnt have great potential for application to the land-use MCC in remote sensing image interpretation. In addition, the introduction of parallel computing into large-scale land use interpretation will help improve the efficiency of solving MCC problems using MaxEnt.
|
Keywords
MaxEnt
land use
one-class classification algorithm
multi-class classification algorithm
remote sensing image interpretation
Yunyan River basin
|
|
Issue Date: 07 July 2023
|
|
|
[1] |
刘南威, 郭有立. 综合自然地理学(第2版)[M]. 北京: 科学出版社, 2004.
|
[1] |
Liu N W, Guo Y L. Synthesize physical geography(2nd edition)[M]. Beijing: Science Press, 2004.
|
[2] |
刘彦随. 区域土地利用优化配置[M]. 北京: 学苑出版社, 1999.
|
[2] |
Liu Y S. Optimal regional allocation of land use[M]. Beijing: Xueyuan Press, 1999.
|
[3] |
舒弥, 杜世宏. 国土调查遥感40年进展与挑战[J]. 地球信息科学学报, 2022, 24(4):597-616.
doi: 10.12082/dqxxkx.2022.210512
|
[3] |
Shu M, Du S H. Forty years’ progress and challenges of remote sensing in national land survey[J]. Journal of Geo-Information Science, 2022, 24(4):597-616.
|
[4] |
Xu L, Herold M, Tsendbazar N E, et al. Time series analysis for global land cover change monitoring:A comparison across sensors[J]. Remote Sensing of Environment, 2022, 271:112905.
doi: 10.1016/j.rse.2022.112905
url: https://linkinghub.elsevier.com/retrieve/pii/S0034425722000190
|
[5] |
赵英时. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003.
|
[5] |
Zhao Y S. Principles and methods of remote sensing for applied anaysis[M]. Beijing: Science Press, 2003.
|
[6] |
Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance[J]. International Journal of Remote Sensing, 2007, 28(5):823-870.
doi: 10.1080/01431160600746456
url: https://www.tandfonline.com/doi/full/10.1080/01431160600746456
|
[7] |
Li W K, Guo Q H. A maximum entropy approach to one-class classification of remote sensing imagery[J]. International Journal of Remote Sensing, 2010, 31(7-8):2227-2235.
doi: 10.1080/01431161003702245
url: https://www.tandfonline.com/doi/full/10.1080/01431161003702245
|
[8] |
Shahzad N, Ding X, Abbas S. A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of northern Pakistan[J]. Applied Sciences, 2022, 12(5):2280-2302.
doi: 10.3390/app12052280
url: https://www.mdpi.com/2076-3417/12/5/2280
|
[9] |
Arabameri A, Asadi N O, Saha S, et al. Novel ensemble approaches of machine learning techniques in modeling the gully erosion susceptibility[J]. Remote Sensing, 2020, 12(11):1890-1918.
doi: 10.3390/rs12111890
url: https://www.mdpi.com/2072-4292/12/11/1890
|
[10] |
Fernández I C, Morales N S. One-class land-cover classification using MaxEnt:The effect of modelling parameterization on classification accuracy[J]. PeerJ, 2019, 7:e7016.
doi: 10.7717/peerj.7016
url: https://peerj.com/articles/7016
|
[11] |
Lin J Y, Liu X P, Li K, et al. A maximum entropy method to extract urban land by combining MODIS reflectance,MODIS NDVI,and DMSP-OLS data[J]. International Journal of Remote Sensing, 2014, 35(17-18):6708-6727.
doi: 10.1080/01431161.2014.960623
url: https://www.tandfonline.com/doi/full/10.1080/01431161.2014.960623
|
[12] |
陈颖彪, 郑子豪, 吴志峰, 等. 夜间灯光遥感数据应用综述和展望[J]. 地理科学进展, 2019, 38(2):205-223.
doi: 10.18306/dlkxjz.2019.02.005
|
[12] |
Chen Y B, Zheng Z H, Wu Z F, et al. Review and prospect of application of nighttime light remote sensing data[J]. Progress in Geo-graphy, 2019, 38(2):205-223.
|
[13] |
白琪阶, 宋志松, 王红瑞, 等. 基于SWAT模型定量分析自然因素与人为因素对水文系统的影响——以漳卫南运河流域为例[J]. 自然资源学报, 2018, 33(9):1575-1587.
doi: 10.31497/zrzyxb.20170882
|
[13] |
Bai Q J, Song Z S, Wang H R, et al. Quantitative analysis of the impact of natural factors and human factors on hydrological system using the SWAT model:The Zhangweinan canal basin case[J]. Journal of Natural Resources, 2018, 33(9):1575-1587.
doi: 10.31497/zrzyxb.20170882
url: http://www.jnr.ac.cn/CN/10.31497/zrzyxb.20170882
|
[14] |
Arnold J G, Williams J R, Maidment D R. Continuous-time water and sediment-routing model for large basins[J]. Journal of Hydraulic Engineering, 1995, 121(2):171-183.
doi: 10.1061/(ASCE)0733-9429(1995)121:2(171)
url: https://ascelibrary.org/doi/10.1061/%28ASCE%290733-9429%281995%29121%3A2%28171%29
|
[15] |
李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012.
|
[15] |
Li H. Statistics learning method[M]. Beijing: Tsinghua University Press, 2012.
|
[16] |
Jaynes E T. Information theory and statistical mechanics[J]. Physical Review, 1957, 106(4):620-630.
doi: 10.1103/PhysRev.106.620
url: https://link.aps.org/doi/10.1103/PhysRev.106.620
|
[17] |
Sanchez-Hernandez C, Boyd D S, Foody G M. One-class classification for mapping a specific land-cover class:SVDD classification of Fenland[J]. IEEE Transactions on Geoence and Remote Sensing, 2007, 45(4):1061-1073.
|
[18] |
Phillips S J, Dudík M, Schapire R E. Maxent software for modeling species niches and distributions(Version 3.4.1)[EB/OL]. [2022-08-22]http://-biodiversityinformatics.amnh.org/open_source/maxent/.
url: http://-biodiversityinformatics.amnh.org/open_source/maxent/
|
[19] |
周珂, 杨永清, 张俨娜, 等. 光学遥感影像土地利用分类方法综述[J]. 科学技术与工程, 2021, 21(32):13603-13613.
|
[19] |
Zhou K, Yang Y Q, Zhang Y N, et al. Review of land use classification methods based on optical remote sensing images[J]. Science Technology and Engineering, 2021, 21(32):13603-13613.
|
[20] |
Belgiu M, Drăguţ L. Random forest in remote sensing:A review of applications and future directions[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114:24-31.
doi: 10.1016/j.isprsjprs.2016.01.011
url: https://linkinghub.elsevier.com/retrieve/pii/S0924271616000265
|
[21] |
Sisodia P S, Tiwari V, Kumar A. Analysis of supervised maximum likelihood classification for remote sensing image[C]// International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014).IEEE, 2014:1-4.
|
[22] |
骆剑承, 王钦敏, 马江洪, 等. 遥感图像最大似然分类方法的EM改进算法[J]. 测绘学报, 2002(3):234-239.
|
[22] |
Luo J C, Wang Q M, Ma J H, et al. The EM-based maximum likelihood classifier for remotely sensed data[J]. Acta Geodaetica et Cartographica Sinica, 2002(3):234-239.
|
[23] |
Otukei J R, Blaschke T. Land cover change assessment using decision trees,support vector machines and maximum likelihood classification algorithms[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12:S27-S31.
doi: 10.1016/j.jag.2009.11.002
url: https://linkinghub.elsevier.com/retrieve/pii/S0303243409001135
|
[24] |
张睿, 马建文. 支持向量机在遥感数据分类中的应用新进展[J]. 地球科学进展, 2009, 24(5):555-562.
doi: 10.11867/j.issn.1001-8166.2009.05.0555
|
[24] |
Zhang R, Ma J W. State of the art on remotely sensed data classification based on support vector machines[J]. Advances in Earth Science, 2009, 24(5):555-562.
doi: 10.11867/j.issn.1001-8166.2009.05.0555
|
[25] |
Thomas N, Hanna M, Tim A, et al. Satellite:Handling and manipulating remote sensing data[EB/OL]. [2021-10-12]https://cran.r-project.org/web/packages/satellite/index.html.
url: https://cran.r-project.org/web/packages/satellite/index.html
|
[26] |
李国庆, 黄菁华, 刘冠, 等. 基于Landsat8卫星影像土地利用景观破碎化研究——以陕西省延安麻塔流域为例[J]. 国土资源遥感, 2020, 32(3):121-128.doi:10.6046/gtzyyg.2020.03.16.
doi: 10.6046/gtzyyg.2020.03.16
|
[26] |
Li G Q, Huang J H, Liu G, et al. A study of the landscape fragmentations of land cover structure based on Landsat8 remote sensing image:A case study of Mata watershed in Yanan,Shaanxi Province[J]. Remote Sensing for Land and Resources, 2020, 32(3):121-128.doi:10.6046/gtzyyg.2020.03.16.
doi: 10.6046/gtzyyg.2020.03.16
|
[27] |
Artstein R, Poesio M. Inter-coder agreement for computational linguistics[J]. Computational Linguistics, 2008, 34(4):555-596.
doi: 10.1162/coli.07-034-R2
url: https://direct.mit.edu/coli/article/34/4/555-596/1999
|
[28] |
Cohen J. A coefficient of agreement for nominal scales[J]. Educational and psychological measurement, 1960, 20(1):37-46.
doi: 10.1177/001316446002000104
url: http://journals.sagepub.com/doi/10.1177/001316446002000104
|
[29] |
Mack B, Waske B. In-depth comparisons of MaxEnt,biased SVM and one-class SVM for one-class classification of remote sensing data[J]. Remote Sensing Letters, 2017, 8(3):290-299.
doi: 10.1080/2150704X.2016.1265689
url: https://www.tandfonline.com/doi/full/10.1080/2150704X.2016.1265689
|
[30] |
许泽宇, 沈占锋, 李杨, 等. 增强型DeepLab算法和自适应损失函数的高分辨率遥感影像分类[J]. 遥感学报, 2022, 26(2):406-415.
|
[30] |
Xu Z Y, Shen Z F, Li Y, et al. Classification of high-resolution remote sensing images based on enhanced Deep Lab algorithm and adaptive loss function[J]. National Remote Sensing Bulletin, 2022, 26(2):406-415.
doi: 10.11834/jrs.20209200
url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20209200/
|
[31] |
吴琳琳, 李晓燕, 毛德华, 等. 基于遥感和多源地理数据的城市土地利用分类[J]. 自然资源遥感, 2022, 34(1):127-134.doi:10.6046/zrzyyg.2021061.
doi: 10.6046/zrzyyg.2021061
|
[31] |
Wu L L, Li X Y, Mao D H, et al. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1):127-134.doi:10.6046/zrzyyg.2021061.
doi: 10.6046/zrzyyg.2021061
|
[32] |
王逸男, 孔祥兵, 赵春敬, 等. 2000—2020年黄土高原植被覆盖度时空格局变化分析[J]. 水土保持学报, 2022, 36(3):130-137.
|
[32] |
Wang Y N, Kong X B, Zhao C J, et al. Change of vegetation coverage in the Loess Plateau from 2000 to 2020 and its spatiotemporal pattern analysis[J]. Journal of Soil and Water Conservation, 2022, 36(3):130-137.
|
[33] |
Li G Q, Huang J H, Guo H, et al. Projecting species loss and turnover under climate change for 111 Chinese tree species[J]. Forest Ecology and Management, 2020, 477:118488.
doi: 10.1016/j.foreco.2020.118488
url: https://linkinghub.elsevier.com/retrieve/pii/S0378112720312573
|
[34] |
Phillips S J, Anderson R P, Schapire R E. Maximum entropy modeling of species geographic distributions[J]. Ecological modelling, 2006, 190(3-4):231-259.
doi: 10.1016/j.ecolmodel.2005.03.026
url: https://linkinghub.elsevier.com/retrieve/pii/S030438000500267X
|
[35] |
Li W, Zhang W, Li Z, et al. A new method for surface water extraction using multi-temporal Landsat8 images based on maximum entropy model[J]. European Journal of Remote Sensing, 2022, 55(1):303-312.
doi: 10.1080/22797254.2022.2062054
url: https://www.tandfonline.com/doi/full/10.1080/22797254.2022.2062054
|
[36] |
Rapinel S, Hubert-Moy L. One-class classification of natural vegetation using remote sensing:A review[J]. Remote Sensing, 2021, 13(10):1892-1915.
doi: 10.3390/rs13101892
url: https://www.mdpi.com/2072-4292/13/10/1892
|
[37] |
Norallahi M, Kaboli H S. Urban flood hazard mapping using machine learning modelings:GARP,RF,MaxEnt and NB[J]. Natural Hazards, 2021, 106(1):119-137.
doi: 10.1007/s11069-020-04453-3
|
[38] |
关雪峰, 曾宇媚. 时空大数据背景下并行数据处理分析挖掘的进展及趋势[J]. 地理科学进展, 2018, 37(10):1314-1327.
doi: 10.18306/dlkxjz.2018.10.002
|
[38] |
Guan X F, Zeng Y M. Research progress and trends of parallel processing,analysis,and mining of big spatiotemporal data[J]. Progress in Geography, 2018, 37(10):1314-1327.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|