Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (2): 485-492.DOI: 10.11772/j.issn.1001-9081.2021020332
• Data science and technology • Previous Articles Next Articles
Dongchang ZHAI1, Hongmei CHEN2()
Received:
2021-03-05
Revised:
2021-04-30
Accepted:
2021-05-07
Online:
2022-02-11
Published:
2022-02-10
Contact:
Hongmei CHEN
About author:
ZHAI Dongchang, born in 1993, M. S. candidate. His research interests include data mining.Supported by:
通讯作者:
陈红梅
作者简介:
翟东昌(1993—),男,甘肃兰州人,硕士研究生,主要研究方向:数据挖掘;基金资助:
CLC Number:
Dongchang ZHAI, Hongmei CHEN. Hyperspectral band selection algorithm based on neighborhood entropy[J]. Journal of Computer Applications, 2022, 42(2): 485-492.
翟东昌, 陈红梅. 基于邻域熵的高光谱波段选择算法[J]. 《计算机应用》唯一官方网站, 2022, 42(2): 485-492.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021020332
类标 | 类名 | 样本数 |
---|---|---|
1 | Alfalfa | 46 |
2 | Corn-notill | 1 428 |
3 | Corn-mintill | 830 |
4 | Corn | 237 |
5 | Grass-pasture | 483 |
6 | Grass-trees | 730 |
7 | Grass-pasture-mowed | 28 |
8 | Hay-windrowed | 478 |
9 | Oats | 20 |
10 | Soybean-notill | 972 |
11 | Soybean-mintill | 2 455 |
12 | Soybean-clean | 593 |
13 | Wheat | 205 |
14 | Woods | 1 265 |
15 | Buildings-Grass-Trees-Drives | 386 |
16 | Stone-Steel-Towers | 93 |
Tab. 1 Class labels and corresponding numbers of samples in Indian Pines dataset
类标 | 类名 | 样本数 |
---|---|---|
1 | Alfalfa | 46 |
2 | Corn-notill | 1 428 |
3 | Corn-mintill | 830 |
4 | Corn | 237 |
5 | Grass-pasture | 483 |
6 | Grass-trees | 730 |
7 | Grass-pasture-mowed | 28 |
8 | Hay-windrowed | 478 |
9 | Oats | 20 |
10 | Soybean-notill | 972 |
11 | Soybean-mintill | 2 455 |
12 | Soybean-clean | 593 |
13 | Wheat | 205 |
14 | Woods | 1 265 |
15 | Buildings-Grass-Trees-Drives | 386 |
16 | Stone-Steel-Towers | 93 |
类标 | 类名 | 样本数 |
---|---|---|
1 | Brocoli_green_weeds_1 | 2 009 |
2 | Brocoli_green_weeds_2 | 3 726 |
3 | Fallow | 1 976 |
4 | Fallow_rough_plow | 1 394 |
5 | Fallow_smooth | 2 678 |
6 | Stubble | 3 959 |
7 | Celery | 3 579 |
8 | Grapes_untrain | 11 271 |
9 | Soil_vinyard_develop | 6 203 |
10 | Corn_senesced_green_weeds | 3 278 |
11 | Lettuce_romaine_4wk | 1 068 |
12 | Lettuce_romaine_5wk | 1 927 |
13 | Lettuce_romaine_6wk | 916 |
14 | Lettuce_romaine_7wk | 1 070 |
15 | Vinyard_untrain | 7 268 |
16 | Vinyard_vertical_trellis | 1 807 |
Tab. 2 Class labels and corresponding numbers of samples in Salinas dataset
类标 | 类名 | 样本数 |
---|---|---|
1 | Brocoli_green_weeds_1 | 2 009 |
2 | Brocoli_green_weeds_2 | 3 726 |
3 | Fallow | 1 976 |
4 | Fallow_rough_plow | 1 394 |
5 | Fallow_smooth | 2 678 |
6 | Stubble | 3 959 |
7 | Celery | 3 579 |
8 | Grapes_untrain | 11 271 |
9 | Soil_vinyard_develop | 6 203 |
10 | Corn_senesced_green_weeds | 3 278 |
11 | Lettuce_romaine_4wk | 1 068 |
12 | Lettuce_romaine_5wk | 1 927 |
13 | Lettuce_romaine_6wk | 916 |
14 | Lettuce_romaine_7wk | 1 070 |
15 | Vinyard_untrain | 7 268 |
16 | Vinyard_vertical_trellis | 1 807 |
算法 | 时间复杂度 |
---|---|
NEFS/SINEFS | O(DNαs2) |
mRMR-MIFS | O(DNs2) |
X-MIFS | O(DNs2) |
NMI | O(DNs log N) |
WaLuMI | O(LDN+L2D) |
Tab. 3 Time complexity of each algorithm
算法 | 时间复杂度 |
---|---|
NEFS/SINEFS | O(DNαs2) |
mRMR-MIFS | O(DNs2) |
X-MIFS | O(DNs2) |
NMI | O(DNs log N) |
WaLuMI | O(LDN+L2D) |
算法 | 系数 | 截距 |
---|---|---|
SINEFS | 1.02×10-5 | 16.600 |
NEFS | 2.61×10-4 | 257.000 |
mRMR-MIFS | 1.57×10-6 | -0.152 |
X-MIFS | 4.8×10-7 | -0.144 |
NMI | 3.8×10-5 | -0.521 |
WaLuMI | 3.2×10-6 | -0.489 |
Tab. 4 CPU time linear model
算法 | 系数 | 截距 |
---|---|---|
SINEFS | 1.02×10-5 | 16.600 |
NEFS | 2.61×10-4 | 257.000 |
mRMR-MIFS | 1.57×10-6 | -0.152 |
X-MIFS | 4.8×10-7 | -0.144 |
NMI | 3.8×10-5 | -0.521 |
WaLuMI | 3.2×10-6 | -0.489 |
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