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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 48-54     DOI: 10.6046/gtzyyg.2018.03.07
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Multi-angle remote sensing image classification based on artificial bee colony algorithm
Xuefeng YANG, Mao YE(), Donglei MAO
College of Geography Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
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Abstract  

Artificial bee colony(ABC)algorithm is widely used in optimization field, but the study of the applications of the remote sensing image classification is inadequate. Through the use of ABC algorithm,the classification system was constructed on the basis of rules. The multi-dimensional data sets consisting of the multi-angle remote sensing observation data originating from the middle and lower reaches of Tarim River were investigated so as to generate the decision rules. A comparison with the classification results of the maximum likelihood method(MLC), C4.5 decision tree and support vector machine(SVM) shows that classification accuracy of ABC is higher than that of MLC and C4.5 overall, but lower than that of SVM. At the same time, through the frequency analysis of the classification attributes in the rules, it is proved that ABC can effectively discover the relationship between the results of the multi-angle data observation and different land cover types.
Keywords artificial bee colony(ABC)algorithm      multi-angle remote sensing      land cover      middle and lower reaches of Tarim River     
:  TP79  
Corresponding Authors: Mao YE     E-mail: 867464686@qq.com
Issue Date: 10 September 2018
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Xuefeng YANG
Mao YE
Donglei MAO
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Xuefeng YANG,Mao YE,Donglei MAO. Multi-angle remote sensing image classification based on artificial bee colony algorithm[J]. Remote Sensing for Land & Resources, 2018, 30(3): 48-54.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.07     OR     https://www.gtzyyg.com/EN/Y2018/V30/I3/48
Fig.1  Flowchart of ABC algorithm
Fig.2  Flowchart of data classification based on ABC
波段 MISR各角度影像波段空间分辨率
DF CF BF AF AN AA BA CA DA
近红外 1 100 1 100 1 100 1 100 275 1 100 1 100 1 100 1 100
红光 275 275 275 275 275 275 275 275 275
蓝光 1 100 1 100 1 100 1 100 275 1 100 1 100 1 100 1 100
绿光 1 100 1 100 1 100 1 100 275 1 100 1 100 1 100 1 100
Tab.1  Spatial resolution of various angle image bands of MISR at globe mode(m)
类型 样地数 覆盖度/% 描述
灌木 1 888 >5 灌木、半灌木
林地 1 148 >5 胡杨林
水体 95 0 水库、天然水体
未利用地 647 <5 沙地、盐碱地
耕地 383 >40 农田
草地 206 >5 盐生草本植物
Tab.2  Types of land cover
类型 灌木 林地 水体 未利
用地		 耕地 草地 总数 用户
精度
灌木 337 138 0 102 6 40 623 0.54
林地 62 266 0 34 1 15 378 0.70
水体 0 0 34 0 0 1 35 0.97
未利用地 22 4 0 187 0 4 217 0.86
耕地 1 0 0 0 130 0 131 0.99
草地 7 5 0 6 0 40 58 0.69
总数 429 413 34 329 137 100 1 442
生产者
精度		 0.79 0.64 1.00 0.57 0.95 0.40
Tab.3  Confusion matrix of MLC classification result
类型 灌木 林地 水体 未利
用地		 耕地 草地 总数 用户
精度
灌木 466 101 0 44 1 11 623 0.75
林地 155 203 0 16 0 4 378 0.54
水体 1 0 34 0 0 0 35 0.97
未利用地 52 12 0 151 0 2 217 0.70
耕地 2 0 0 0 126 3 131 0.96
草地 17 5 0 4 1 31 58 0.53
总数 693 321 34 215 128 51 1 442
生产者
精度		 0.67 0.63 1.00 0.70 0.98 0.61
Tab.4  Confusion matrix of C4.5 classification result
类型 灌木 林地 水体 未利
用地		 耕地 草地 总数 用户
精度
灌木 490 93 0 32 0 8 623 0.79
林地 150 210 0 16 0 2 378 0.56
水体 2 0 33 0 0 0 35 0.94
未利用地 65 9 0 143 0 0 217 0.66
耕地 2 0 0 0 127 2 131 0.97
草地 30 1 0 1 0 26 58 0.45
总数 739 313 33 192 127 38 1 442
生产者
精度		 0.66 0.67 1.00 0.74 1.00 0.68
Tab.5  Confusion matrix of ABC classification result
类型 灌木 林地 水体 未利
用地		 耕地 草地 总数 用户
精度
灌木 498 96 0 22 1 6 623 0.80
林地 104 259 0 9 0 6 378 0.69
水体 0 0 27 0 8 0 35 0.77
未利用地 46 12 0 157 1 1 217 0.72
耕地 1 0 0 1 129 0 131 0.98
草地 10 5 0 1 0 42 58 0.72
总数 659 372 27 190 139 55 1 442
生产者
精度		 0.76 0.70 1.00 0.83 0.93 0.76
Tab.6  Confusion matrix of SVM classification result
Fig.3  Frequency distribution curves of ABC rule’s attributes
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