神经网络敏感性分析的高光谱遥感影像降维与分类方法

高红民; 李臣明; 周惠; 张振; 陈玲慧; 何振宇

doi:10.11999/JEIT160052

神经网络敏感性分析的高光谱遥感影像降维与分类方法

doi: 10.11999/JEIT160052

基金项目:

中央高校基本科研业务费项目(2014B13214, 2015B 26914)，十二五国家科技支撑计划项目(2015BAB07B03)，河海大学国家级大学生创新训练计划项目(201610294061)

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出版历程
- 收稿日期: 2016-01-13
- 修回日期: 2016-06-08
- 刊出日期: 2016-11-19

Dimension Reduction and Classification of Hyperspectral Remote Sensing Images Based on Sensitivity Analysis of Artificial Neural Network

Funds:

The Fundamental Research Funds for the Central Universities (2014B13214, 2015B26914), The Projects in the National Science Technology Pillar Program during the Twelfth Five-year Plan Period (2015BAB07B03), The National Undergraduate Training Program for Innovation and Entrepreneurship of Hohai University (201610294061)

摘要

摘要: 高光谱遥感影像由于其巨大的波段数直接导致信息的高冗余和数据处理的复杂，这不仅带来庞大的计算量，而且会损害分类精度。因此，在对高光谱影像进行处理、分析之前进行降维变得非常必要。神经网络敏感性分析可以用于对模型的简化降维，该文将该方法运用于高光谱遥感影像降维中，通过子空间划分弱化波段之间的相关性，利用差分进化算法(DE)优化神经网络结构，采用Ruck敏感性分析方法剔除掉对分类贡献较小的波段，从而实现降维。最后，采用AVIRIS影像进行实验，所提算法相比其他相近的降维与分类方法能获得更高的分类精度，达到85.83%，比其他相近方法中最优方法高出0.31%。
- 神经网络敏感性分析 /
- 高光谱遥感影像降维 /
- 子空间划分 /
- 差分进化 /
- Ruck敏感性分析
Abstract: The high dimensions of hyperspectral remote sensing images will cause the redundancy of information and complexity of data processing, which also brings tremendous computing workload and damages application accuracy. Therefore, before the analysis of hyperspectral image processing, it is necessary to reduce the high dimensions of hyperspectral data. The Sensitivity Analysis (SA) of artificial neural network can be used in dimension reduction of the model. Now the Sensitivity Analysis of artificial neural network is applied to dimension reduction for hyperspectral remote sensing images in the paper. First of all, all bands are divided into several groups as long as a lower correlation exists between adjacent bands. Furthermore, Differential Evolution (DE) algorithm is used for optimizing neural network structure. Moreover, the bands which make small contribution will be given up based on Ruck sensitivity analysis method. Finally, experiments are conducted with AVIRIS images. The results show that the proposed method can get high classification accuracy of 85.83% at small training samples, 0.31% higher than the best one among other similar methods of dimension reduction and classification.

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