Spectroscopy and Spectral Analysis, Volume. 42, Issue 10, 3283(2022)
Construction and Application of ReliefF-RFE Feature Selection Algorithm for Hyperspectral Image Classification
Figures & Tables(10)
Fig. 1. Hyperspectral images of each standard dataset
(a): Indian pines; (b): Salinas-A; (c): KSC
Fig. 2. References of real features for each standard dataset
(a): Indian pines; (b): Salinas-A; (c): KSC
Fig. 3. Classification results of three feature selection algorithms for hyperspectral images of Indian pines dataset
Fig. 4. Classification results of three feature selection algorithms for hyperspectral images of Salinas-A dataset
Fig. 5. Classification results of three feature selection algorithms for hyperspectral images of KSC dataset
Fig. 6. Comprehensive comparison of three feature selection algorithms
(a): Classification accuracy; (b): Feature dimension and runtime
Table 1. Hyperspectral dataset description
View tableView in ArticleTable 1. Hyperspectral dataset description
数据集 Indian pines Salinas-A KSC 特征维数 200 204 176 样本数 10 249 5 348 5 211 影像尺寸 145×145 86×83 512×614 分辨率/m 20 3.7 18 地物类别 16 6 13
Table 2. Classification results based on the best feature subset of the Indian pines dataset
View tableView in ArticleTable 2. Classification results based on the best feature subset of the Indian pines dataset
算法 OA
/%F-measure
/%Kappa
系数特征
维数t
/hReliefF 85.53 85.15 0.834 0 157 58.56 RFE 86.41 86.10 0.844 2 36 107.33 ReliefF-RFE 86.28 86.00 0.842 2 45 91.58
Table 3. Classification results based on the best feature subset of the Salinas-A dataset
View tableView in ArticleTable 3. Classification results based on the best feature subset of the Salinas-A dataset
算法 OA
/%F-measure
/%Kappa
系数特征
维数t
/hReliefF 99.31 99.32 0.991 4 180 6.34 RFE 99.31 99.32 0.991 4 35 11.35 ReliefF-RFE 99.38 99.38 0.992 2 71 8.63
Table 4. Classification results with the best feature subset of the KSC dataset
View tableView in ArticleTable 4. Classification results with the best feature subset of the KSC dataset
算法 OA
/%F-measure
/%Kappa
系数特征
维数t
/hReliefF 93.22 93.14 0.924 0 145 8.22 RFE 93.41 93.31 0.936 2 72 14.98 ReliefF-RFE 93.16 93.06 0.923 8 64 9.58
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. Construction and Application of ReliefF-RFE Feature Selection Algorithm for Hyperspectral Image Classification[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3283
Paper Information
Category: Research Articles
Received: Oct. 10, 2021
Accepted: Jan. 16, 2022
Published Online: Nov. 23, 2022
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