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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (4) : 41-48     DOI: 10.6046/gtzyyg.2018.04.07
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Hyperspectral similar sample classification algorithm based on Fisher criterion and TrAdaboost
Wanjun LIU, Tianhui LI(), Haicheng QU
School of Software, Liaoning Technical University, Huludao 125105, China
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Abstract  

To tackle the low classification accuracy problem of hyperspectral image classification caused by similar samples under the condition of small-sample-size, this paper proposes a hyperspectral image classification algorithm based on Fisher criterion and TrAdboost (H_TrAdaboost). Firstly, an auxiliary sample is determined with the spectral angle mapping (SAM) method and spectral information divergence (SID) so as to improve the total number of training samples. Secondly, the samples are studied separability based on the improved Fisher criterion to obtain relatively strong samples set. Finally, the weight of positive and negative sample distribution is adjusted dynamically by using TrAdaboost algorithm so as to achieve hyperspectral similarity class classification in the small sample size problem. In the comparative experiments with other compared algorithms, the highest classification accuracy is achieved, which fully shows that H_TrAdaboost algorithm can well solve the similar hyperspectral image classifications.
Keywords hyperspectral image classification      spectral angle mapping (SAM)      spectral information divergence (SID)      Fisher criterion     
:  TP751  
Corresponding Authors: Tianhui LI     E-mail: 163250335@qq.com
Issue Date: 07 December 2018
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Wanjun LIU
Tianhui LI
Haicheng QU
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Wanjun LIU,Tianhui LI,Haicheng QU. Hyperspectral similar sample classification algorithm based on Fisher criterion and TrAdaboost[J]. Remote Sensing for Land & Resources, 2018, 30(4): 41-48.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.04.07     OR     https://www.gtzyyg.com/EN/Y2018/V30/I4/41
Fig.1  Contrast of spectral mean and standard deviation of similar features
Fig.2  Flowchart of H_TrAdaboost algorithm
Fig.3  Ground truth of Indian Pines dataset
类别 类别图例 名称 总样本数/个
C1 免耕玉米地 1 428
C2 玉米幼苗 830
C3 玉米 237
C4 草地-树木 730
C5 免耕大豆地 972
C6 大豆幼苗 2 455
C7 修剪大豆地 593
C8 木材 1 265
  
类别 C1 C2 C3 C4 C5 C6 C7 C8
C1 0.000 0 0.000 2 0.000 5 0.002 9 0.000 2 0.000 3 0.000 4 0.010 8
C2 0.000 2 0.000 0 0.000 4 0.002 5 0.000 3 0.000 2 0.000 3 0.009 9
C3 0.000 5 0.000 4 0.000 0 0.001 6 0.000 5 0.000 5 0.000 7 0.006 9
C4 0.002 9 0.002 5 0.001 6 0.000 0 0.003 2 0.002 7 0.002 8 0.001 4
C5 0.000 2 0.000 3 0.000 5 0.003 2 0.000 0 0.000 4 0.000 3 0.011 7
C6 0.000 3 0.000 2 0.000 5 0.002 7 0.000 4 0.000 0 0.000 4 0.011 1
C7 0.000 4 0.000 3 0.000 7 0.002 8 0.000 3 0.000 4 0.000 0 0.010 9
C8 0.010 8 0.009 9 0.006 9 0.001 4 0.011 7 0.011 1 0.010 9 0.000 0
Tab.2  SID_SA value of each category
目标样
本类别		 辅助样
本类别		 SVM TrAdaboost H_TrAdaboost
C1 vs C2 C6 vs C5 54.50 55.50 59.00
C1 vs C2 C5 vs C6 63.50 67.00 69.50
C1 vs C5 C2 vs C6 73.50 74.50 77.50
C1 vs C5 C2 vs C7 71.00 68.50 76.00
C1 vs C6 C2 vs C7 65.50 67.00 71.00
C1 vs C6 C7 vs C2 70.50 72.50 73.00
C2 vs C5 C1 vs C6 71.00 74.50 75.50
C2 vs C8 C1 vs C4 98.50 99.50 99.50
C2 vs C8 C1 vs C5 94.00 97.00 97.50
Tab.3  Accuracy compare of each algorithm(%)
样本类别 SVM算法
分类结果		 TrAdaboost算
法分类结果		 H_TrAdaboost算
法分类结果
C1 vs C2
C1 vs C5
C1 vs C6
C2 vs C5
C2 vs C8
  
算法 统计值 15% 10% 5% 3% 2% 1%
SVM OA/% 71.00 71.00 69.00 65.50 63.50 63.50
Kappa 0.70 0.71 0.67 0.64 0.63 0.62
TrAdaboost OA/% 77.50 76.00 71.00 69.50 68.50 67.00
Kappa 0.77 0.73 0.71 0.68 0.68 0.65
H_TrAdaboost OA/% 79.50 77.50 73.50 72.00 70.50 69.50
Kappa 0.79 0.77 0.73 0.72 0.70 0.69
Tab.5  Accuracy compare of each algorithm in sample 1
算法 统计值 15% 10% 5% 3% 2% 1%
SVM OA/% 77.00 76.00 73.00 71.50 70.00 70.50
Kappa 0.76 0.76 0.73 0.71 0.70 0.70
TrAdaboost OA/% 79.50 77.50 75.00 72.50 73.50 72.50
Kappa 0.77 0.77 0.75 0.73 0.73 0.72
H_TrAdaboost OA/% 80.50 79.00 76.50 74.50 73.50 73.00
Kappa 0.81 0.79 0.76 0.74 0.73 0.73
Tab.6  Accuracy compare of each algorithm in sample 2
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