结合变异粒子群和字典学习的遥感影像去噪
针对在线字典学习需将所有字典原子全部更新、优化方向难以进行估算等原因造成精度下降的不足，提出基于变异粒子群优化的在线字典学习算法。算法基于ODL的基础，在字典学习的迭代过程中对梯度下降函数进行优化。首先选出特殊字典原子，利用各个字典原子之间关系，线性表征当前选出的原子，以线性系数作为粒子群中的粒子位置。然后将基于变异粒子群的原子更新模式引入字典学习,利用变异粒子群优化算法进行粒子的适应度淘汰，选择更适合的粒子进行下一轮的字典更新。此外，利用中间变量将历史参考数据引入变异粒子群模型以引导其优化方向，提高字典的准确性和有效性。利用高分一号遥感影像进行实验，实验结果表明该算法优于同类方法，有更好的噪...
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| Published in | 计算机工程与科学 Vol. 39; no. 9; pp. 1675 - 1681 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国科学院大学,北京100049%中国科学院遥感与数字地球研究所,北京,100094
2017
中国科学院遥感与数字地球研究所,北京100094 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-130X |
| DOI | 10.3969/j.issn.1007-130X.2017.09.013 |
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| Summary: | 针对在线字典学习需将所有字典原子全部更新、优化方向难以进行估算等原因造成精度下降的不足，提出基于变异粒子群优化的在线字典学习算法。算法基于ODL的基础，在字典学习的迭代过程中对梯度下降函数进行优化。首先选出特殊字典原子，利用各个字典原子之间关系，线性表征当前选出的原子，以线性系数作为粒子群中的粒子位置。然后将基于变异粒子群的原子更新模式引入字典学习,利用变异粒子群优化算法进行粒子的适应度淘汰，选择更适合的粒子进行下一轮的字典更新。此外，利用中间变量将历史参考数据引入变异粒子群模型以引导其优化方向，提高字典的准确性和有效性。利用高分一号遥感影像进行实验，实验结果表明该算法优于同类方法，有更好的噪音抑制效果，同时也提高了大规模的遥感图像处理性能。 |
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| Bibliography: | WANG Xiao-yan1,2，CHI Tian-he1 （1.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094;2.University of Chinese Academy of Sciences,Beijing 100049） 43-1258/TP Online dictionary learning （ODL） updates all dictionary atoms and it is difficult to estimate the optimization direction, so the accuracy is decreased. Aiming at this drawback, we propose a method of online dictionary learning based on modified particle swarm optimization （MPSO）. On the basis of ODL, the algorithm optimizes the gradient descent function in the iterative process of dictionary learning. A special atom is selected by a rule in the dictionary-updating stage, which is linearly represented by the other atoms in the dictionary. The coefficient of the linear representation is the position of the particles in the MPSO, which is introduced to eliminate the particles by their fitness while leaving the more suitable particles in the next iteration. Furthermore, intermediate variables, which carry the prior reference |
| ISSN: | 1007-130X |
| DOI: | 10.3969/j.issn.1007-130X.2017.09.013 |