Combination strategy of active learning for hyperspectral images classification

Ying CUI; Kai XU; Zhongjun LU; Shubin LIU; Liguo WANG

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

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Combination strategy of active learning for hyperspectral images classification

Papers | 更新时间：2024-06-05

- Combination strategy of active learning for hyperspectral images classification
- Journal on Communications Vol. 39, Issue 4, Pages: 91-99(2018)
- 作者机构：
  
  1. 哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
  2. 黑龙江省农业科学院遥感技术中心，黑龙江哈尔滨 150086
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61675051);Education Ministry Doctoral Research Foundation of China(20132304110007)
- DOI：10.11959/j.issn.1000-436x.2018067
  CLC： TP302
- Online First：2018-04，
  
  Published：25 April 2018
- 稿件说明：
移动端阅览
Ying CUI, Kai XU, Zhongjun LU, et al. Combination strategy of active learning for hyperspectral images classification[J]. Journal on Communications, 2018, 39(4): 91-99.
DOI：

Ying CUI, Kai XU, Zhongjun LU, et al. Combination strategy of active learning for hyperspectral images classification[J]. Journal on Communications, 2018, 39(4): 91-99. DOI： 10.11959/j.issn.1000-436x.2018067.

摘要

针对传统主动学习单一策略算法在挑选最有价值未标记样本时出现的抖动和不稳定的现象，引入集成学习（ensemble learning）分类器的加权组合思想，提出一种基于组合策略的联合挑选（ESAL）方法，将模型的组合衍生至策略的组合，从而实现单一模型多策略的融合，获得更高的稳定性。通过对高光谱遥感图像分类结果的分析可以看出，在获得相同精度阈值时，ESAL 算法相对于单一策略算法最高可节省成本 25.4%，抖动频率减少至原来的16.67%，抖动明显改善，体现出ESAL算法良好的稳定性。

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.

关键词

Keywords

references

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