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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 140-148     DOI: 10.6046/zrzyyg.2022136
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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
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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     
ZTFLH:  TP75  
Issue Date: 07 July 2023
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Dongyang XIONG
Lin ZHANG
Guoqing LI
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Dongyang XIONG,Lin ZHANG,Guoqing LI. MaxEnt-based multi-class classification of land use in remote sensing image interpretation[J]. Remote Sensing for Natural Resources, 2023, 35(2): 140-148.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022136     OR     https://www.gtzyyg.com/EN/Y2023/V35/I2/140
Fig.1  Overview of the study area
传感器
类型		 波段 波长范
围/μm		 空间分
辨率/m		 主要应用领域
陆地成像仪OLI B2 Blue
(蓝光波段)		 0.450~0.515 30 用于水体穿透、分辨植被和土壤等
B3 Green
(绿光波段)		 0.525~0.600 30 用于分辨植被等
B4 Red
(红光波段)		 0.630~0.680 30 用于观测道路、裸露土壤和植被等
B5 NIR
(近红外波段)		 0.845~0.885 30 用于估算生物量、分辨潮湿土壤等
B6 SWIR 1
(短波红外1波段)		 1.560~1.660 30 用于分辨道路、土壤和水等
B7 SWIR 2
(短波红外2波段)		 2.100~2.300 30 用于矿物识别、分辨植被和潮湿土壤等
Tab.1  Landsat8 OLI B2—B7 parameter characteristics
Fig.2  Procedure of multi-classification for land use based on maximum entropy model
Kappa 一致性程度
[-1.00, 0) 极差
[0, 0.20) 微弱
[0.20, 0.40) 弱
[0.40, 0.60) 中度
[0.60, 0.80) 高度
[0.80, 1.00] 极强
Tab.2  Agreement standard of Kappa coefficient evaluation
Fig.3  Land use classification maps of four algorithms
指标 算法
MaxEnt RF MLC SVM
OA/% 84.06 80.88 75.76 79.71
Kappa 0.80 0.76 0.69 0.75
Tab.3  OA and Kappa coefficients of the four algorithms
土地利用类型 MaxEnt RF MLC SVM
灵敏度 特异度 灵敏度 特异度 灵敏度 特异度 灵敏度 特异度
草地 0.93 0.89 0.79 0.93 0.38 0.96 0.93 0.82
耕地 0.85 0.96 0.69 0.96 0.75 0.91 0.85 0.98
灌木 0.46 0.98 0.62 0.91 0.67 0.89 0.15 0.98
建设用地 0.93 1.00 0.93 1.00 1.00 0.94 1.00 1.00
森林 1.00 0.96 1.00 0.96 0.93 1.00 1.00 0.96
Tab.4  Comparison of classification accuracy of four algorithms
Fig.4  Kappa consistency test and confusion matrix of classification results of four classification algorithms
Fig.5  Area of different land use types in Yunyan River watershed
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