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Active Learning Algorithms for the Classification of Hyperspectral Sea Ice Images

机译：高光谱海冰图像分类的主动学习算法

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摘要

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 k-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.
机译：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。高光谱图像适用于监测海冰，它包含连续的光谱信息并且具有更好的目标识别能力。高光谱图像分类的主要瓶颈是需要大量标记的训练样本。但是，标记样品的收集既费时又费钱。为了解决这个问题，我们将主动学习（AL）算法应用于高光谱海冰检测中，该算法可以选择信息量最大的样本。此外，我们提出了一种新颖的基于两种标准评估的研究AL算法：不确定性和多样性。不确定性标准基于两个类别的概率之间的差异，该两个类别具有最高的估计概率，而多样性标准则基于核k均值聚类技术。在2014年4月12日在格陵兰西北部的巴芬湾进行的实验中，与其他AL算法和随机抽样相比，我们提出的AL算法达到了89.327％的最高分类精度，而在达到相同分类精度的同时，该AL算法需要更少的标记成本。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第11期|124601.1-124601.10|共10页
  • 作者

    Han Yanling; Ren Jing; Hong Zhonghua; Zhang Yun; Zhang Long; Meng Wanting; Gu Qiming;

  • 作者单位

    Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China.;

    Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China.;

    Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China.;

    Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China.;

    Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China.;

    Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China.;

    Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China.;

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