主动学习策略融合算法在高光谱图像分类中的应用

doi:10.11959/j.issn.1000-436x.2018067

Abstract

Abstract:

In order to improve the phenomena of jitter and instability of the traditional active learning single strategy algorithm in selecting the most valuable unlabeled samples.The idea of weighted combination of ensemble learning classifier and proposes a joint selection based on the combination strategy method (ESAL,ensemble strategy active learning) was introduced,the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability.By analyzing the classification results of hyperspectral remote sensing images,the ESAL algorithm can save 25.4% of the cost compared with the single strategy algorithm and reduce the jitter frequency to 16.67% when the same accuracy threshold is obtained,and the jitter is obviously improved.ESAL algorithm is out of good stability.

Key words: active learning, ensemble learning, hyperspectral image, strategy combination

CLC Number:

TP302

Ying CUI,Kai XU,Zhongjun LU,Shubin LIU,Liguo WANG. Combination strategy of active learning for hyperspectral images classification[J]. Journal on Communications, 2018, 39(4): 91-99.

Figures/Tables 9

References 18

[1]	WEI D M M C . Active learning via multi-view and local proximity co-regularization for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2011,5(3): 618-628.
[2]	MITRA P , UMA SHANKAR B , PAL S K . Segmentation of multispectral remote sensing images using active support vector machines[J]. Pattern Recognition Letters, 2004,25(9): 1067-1074.
[3]	JOSHI A J , PORIKLI F , PAPANIKOLOPOULOS N . Multi-class active learning for image classification[C]// 2009 IEEE Symposium on The Computer Vision and Pattern Recognition. 2009: 2372-2379.
[4]	TUIA D , VOLPI M , COPA L ,et al. A Survey of active learning algorithms for supervised remote sensing image classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2011,5(3): 606-617.
[5]	李宠, 谷琼, 蔡之华 . 改进的主动学习算法及在高光谱分类中的应用[J]. 华中科技大学学报, 2013,41(11): 274-278.
	LI C , GU Q , CAI Z H . Improved active learning algorithm and its application in hyperspectral classification[J]. Journal of Huazhong University of Science and Technology, 2013,41(11): 274-278.
[6]	LI M , WANG R , TANG K . Combining semi-supervised and active learning for hyperspectral image classification[C]// 2013 IEEE Symposium on The Computational Intelligence and Data Mining (CIDM). 2013: 89-94.
[7]	WAN L , TANG K , LI M ,et al. Collaborative active and semisupervised learning for hyperspectral remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(5): 2384-2396.
[8]	WANG Z , DU B , ZHANG L ,et al. A novel semisupervised active-learning algorithm for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017,55(6): 3071-3083.
[9]	王立国, 李阳 . 融合主动学习的高光谱图像半监督分类[J]. 哈尔滨工程大学学报, 2017,38(8): 1322-1327.
	WANG L G , LI Y . Semi-supervised classification for hyperspectral image collaborating with active learning algorithm[J]. Journal of Harbin Engineering University, 2017,38(8): 1322-1327.
[10]	SAMIAPPAN S , MOORHEAD R J . Semi-supervised co-training and active learning framework for hyperspectral image classification[C]// the Geoscience and Remote Sensing Symposium (IGARSS), 2015: 401-404.
[11]	王立国, 杨月霜, 刘丹凤 . 基于改进三重训练算法的高光谱图像半监督分类[J]. 哈尔滨工程大学学报, 2016,37(6): 849-854.
	WANG L G YANG Y S , LIU D F . Semi-supervised classification for hyperspectral image based on improved tri-training method[J]. Journal of Harbin Engineering University, 2016,37(6): 849-854.
[12]	赵建华, 刘宁 . 结合主动学习策略的半监督分类算法[J]. 计算机应用研究, 2015,32(8): 2295-2298.
	ZHAO J H , LIU N . Semi-supervised classification algorithm based on active learning strategies[J]. Application Research of Computers, 2015,32(8): 2295-2298.
[13]	ZHANG Z , PASOLLI E , CRAWFORD M M ,et al. An active learning framework for hyperspectral image classification using hierarchical segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016,9(2): 640-654.
[14]	陈荣, 曹永锋, 孙洪 . 基于主动学习和半监督学习的多类图像分类[J]. 自动化学报, 2011,37(8): 954-962.
	CHEN R , CAO Y F , SUN H . Multi-class image classification with active learning and semi-supervised learning[J]. Acta Automatica Sinica, 2011,37(8): 954-962.
[15]	韩松来, 张辉, 周华平 . 基于关联度函数的决策树分类算法[J]. 计算机应用, 2005,25(11): 2655-2657.
	HAN S L , ZHANG H , ZHOU H P . Association function algorithm for decision tree[J]. Journal of Computer Applications, 2005,25(11): 2655-2657.
[16]	QUINLAN J R . Induction of decision trees[J]. Machine Learning, 1986,1(1): 81-106.
[17]	LIU Y , YAO X . Ensemble learning via negative correlation[J]. Neural Networks, 1999,12(10): 1399-1404.
[18]	DIETTERICH T G . Ensemble learning[J]. The Handbook of Brain Theory and Neural Networks, 2002,2: 110-125.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	单基策略/次	ESAL/次
Indian Pines	12	2
KSC	6	1

OA		数据集标记成本数量/个
OA	aEQB	BvSB	ESAL
0.73	145	145	125
0.74	195	195	165
0.75	205	215	185
0.76	235	235	215
0.77	255	245	235
0.78	295	265	255
0.79	345	305	295
0.80	405	335	335
0.81	445	405	385
0.82	515	485	435
0.83	595	595	545
0.84	685	745	665
0.85	825	905	795

OA		数据集标记成本数量/个
OA	aEQB	BvSB	ESAL
0.81	5	5	5
0.82	15	15	15
0.83	15	15	15
0.84	25	25	15
0.85	35	25	25
0.86	45	35	25
0.87	55	45	45
0.88	75	55	55
0.89	95	85	65
0.90	115	95	85
0.91	145	125	105
0.92	175	195	145
0.93	315	285	235

Combination strategy of active learning for hyperspectral images classification

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 18

Related Articles 11

Metrics

Recommended 0

[1]	Qianyi DAI, Bin ZHANG, Song GUO, Kaiyong XU. Blockchain network layer anomaly traffic detection method based on multiple classifier integration [J]. Journal on Communications, 2023, 44(3): 66-80.
[2]	Hongmin GAO, Xueying CAO, Zhonghao CHEN, Zaijun HUA, Chenming LI, Yue CHEN. Hyperspectral image classification method based on multi-scale proximal feature concatenate network [J]. Journal on Communications, 2021, 42(2): 92-102.
[3]	Zunwen HE, Shuai HOU, Wancheng ZHANG, Yan ZHANG. Multi-feature fusion classification method for communication specific emitter identification [J]. Journal on Communications, 2021, 42(2): 103-112.
[4]	Xiaochuan SUN,Zhigang LI,Minghui ZHANG,Guan GUI. Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning [J]. Journal on Communications, 2020, 41(9): 1-7.
[5]	Hongmin GAO,Xueying CAO,Yao YANG,Zaijun HUA,Chenming LI. Application of bilateral fusion model based on CNN in hyperspectral image classification [J]. Journal on Communications, 2020, 41(11): 132-140.
[6]	Tao LI,Yuanbo GUO,Ankang JU. Knowledge triple extraction in cybersecurity with adversarial active learning [J]. Journal on Communications, 2020, 41(10): 80-91.
[7]	. Research on network anomaly detection based on one-class SVM and active learning [J]. Journal on Communications, 2015, 36(11): 136-146.
[8]	Xiao-dong YU,Ying-jie LEI,Ya-fei SONG,Shao-hua YUE,Jun-hong HU. Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition [J]. Journal on Communications, 2015, 36(10): 165-171.
[9]	Jian WU,Sheng-li SHENG,Peng-peng ZHAO,Zhi-ming CUI. Minimal difference sampling for active learning image classification [J]. Journal on Communications, 2014, 35(1): 107-114.
[10]	Yi-peng WANG,Xiao-chun YUN,Yong-zheng ZHANG,Shu-hao LI. Network protocol identification based on active learning and SVM algorithm [J]. Journal on Communications, 2013, 34(10): 135-142.
[11]	. Network protocol identification based on active learning and SVM algorithm [J]. Journal on Communications, 2013, 34(10): 16-142.