Active Learning Algorithms for the Classification of Hyperspectral Sea Ice Images
Active Learning Algorithms for the Classification of Hyperspectral Sea Ice Images
复制标题
高光谱海冰图像分类的主动学习算法
DOI：
10.1155/2015/124601
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发表时间：
2015
影响因子：
--
通讯作者：
Gu Qiming
中科院分区：
文献类型：
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作者：
Han Yanling;Ren Jing;Hong Zhonghua;Zhang Yun;Zhang Long;Meng Wanting;Gu Qiming
海冰是最严重的海洋灾害之一，特别是在极地和高纬度地区。高光谱图像含有连续的光谱信息，具有较好的目标识别能力，适合海冰监测。高光谱图像分类的主要瓶颈是需要大量的标记训练样本。然而，收集有标签的样品既耗时又昂贵。为了解决这一问题，我们将主动学习（AL）算法应用到高光谱海冰检测中，该算法可以选择信息量最大的样本。此外，我们提出了一种新的基于不确定性和多样性两个评价标准的人工智能算法。不确定性准则基于估计概率最高的两个类别的概率之差，而多样性准则基于核均值聚类技术。在2014年4月12日格陵兰西北部Baffin Bay的实验中，与其他人工智能算法和随机抽样相比，本文提出的人工智能算法的分类准确率最高，达到89.327%，在达到相同的分类精度的同时，本文提出的人工智能算法需要较少的标注成本。
Sea ice is one of the most critical marine disasters, especially in the polar and high latitude regions. Hyperspectral image is suitable for monitoring the sea ice, which contains continuous spectrum information and has better ability of target recognition. The principal bottleneck for the classification of hyperspectral image is a large number of labeled training samples required. However, the collection of labeled samples is time consuming and costly. In order to solve this problem, we apply the active learning (AL) algorithm to hyperspectral sea ice detection which can select the most informative samples. Moreover, we propose a novel investigated AL algorithm based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is based on the difference between the probabilities of the two classes having the highest estimated probabilities, while the diversity criterion is based on a kernel -means clustering technology. In the experiments of Baffin Bay in northwest Greenland on April 12, 2014, our proposed AL algorithm achieves the highest classification accuracy of 89.327% compared with other AL algorithms and random sampling, while achieving the same classification accuracy, the proposed AL algorithm needs less labeling cost.