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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参考文献(26)

杜培军, 谭琨, 夏俊士. 高光谱遥感影像分类与支持向量机应用研究[M]. 北京: 科学出版社, 2012: 6-35.

DU Peijun, TAN Kun, and XIA Junshi. Classification of Hyperspectral Remote Sensing Images and Applied Research of SVM[M]. Beijing: Science Press, 2012: 6-35.

童庆禧, 张兵, 郑兰芬. 高光谱遥感原理、技术及应用[M]. 北京: 高等教育出版社, 2006: 33-56.

TONG Qingxi, ZHANG Bing, and ZHENG Lanfen. Hyperspectral Remote Sensing-Principles, Techniques and Applications[M]. Beijing: Higher Education Press, 2006: 33-56.

吴倩, 张荣, 徐大卫. 基于稀疏表示的高光谱数据压缩算法[J]. 电子与信息学报, 2015, 37(1): 78-84. doi: 10.11999/ JEIT140214.

WU Qian, ZHANG Rong, and XU Dawei. Hyperspectral data compression based on sparse representation[J]. Journal of Electronics Information Technology, 2015, 37(1): 78-84. doi: 10.11999/JEIT140214.

GAO Hongmin, XU Lizhong, LI Chenming, et al. A new feature selection method for hyperspectral image classification based on simulated annealing genetic algorithm and choquet fuzzy integral[J]. Mathematical Problems in Engineering, 2013: 1-14. doi: 10.1155/2013/537268.

GAO Lianru, LI Jun, KHODADADZADEH M, et al. Subspace-based support vector machines for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(2): 349-353. doi: 10.1109/LGRS.2014. 2341044.

GURRAM P and KWON H. Coalition game theory based feature subset selection for hyperspectral image classification [C]. IEEE International Geoscience and Remote Sensing Symposium, Quebec, Canada, 2014: 3446-3449.

FALCO N, BENEDIKTSSON J A, and BRUZZONE L. A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2183-2199. doi: 10.1109/JSTARS.2014.2329792.

姜宇, 肖鸿, 刘兴鹏, 等. BP神经网络在异向介质基本结构分析中的应用[J]. 电子与信息学报, 2010, 32(1): 195-198. doi: 10.3724/SP.J.1146.2008.01703.

JIANG Yu, XIAO Hong, LIU Xingpeng, et al. Applications of BP neural network in analyzing metamaterials elemental basic structure[J]. Journal of Electronics Information Technology, 2010, 32(1): 195-198. doi: 10.3724/SP.J.1146. 2008.01703.

张兵, 高连如. 高光谱图像分类与目标探测[M]. 北京: 科学出版社, 2011: 85-101.

ZHANG Bing, GAO Lianru. Hyperspectral Image Classification and Target Detection[M]. Beijing: Science Press, 2011: 85-101.

蔡毅, 邢岩, 胡丹. 敏感性分析综述[J]. 北京师范大学学报(自然科学版), 2008, 44(1): 9-16.

CAI Yi, XING Yan, and HU Dan. On sensitivity analysis[J]. Journal of Beijing Normal University(Natural Science), 2008, 44(1): 9-16.

张军, 刘祖强, 张正禄, 等. 基于神经网络和模糊评判的滑坡敏感性分析[J]. 测绘科学, 2012, 37(3): 59-62.

ZHANG Jun, LIU Zuqiang, ZHANG Zhenglu, et al. Susceptibility of landslide based on artificial neural networks and fuzzy evaluating model[J]. Science of Surveying and Mapping, 2012, 37(3): 59-62.

ZHANG Junping, ZHANG Ye, ZOU Bin, et al. Fusion classification of hyperspectral image based on adaptive subspace decomposition[C]. IEEE International Conference on Image Processing, Vancouver, BC, Canada, 2000, 3: 472-475.

YU Feng and XU Xiaozhong. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network[J]. Applied Energy, 2014, 134: 102-113. doi: 10.1016/j.apenergy.2014.07.104.

LIU Ruixin, ZHANG Xiaodong, ZHANG Lu, et al. Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network[J]. Experimental and Therapeutic Medicine, 2014, 7(6): 1696-1702. doi: 10.3892/etm.2014.1614.

钱文江, 李同春, 丁林. 基于改进BP神经网络的库区渗漏量敏感性分析[J]. 三峡大学学报(自然科学版), 2012, 34(6): 23-27.

QIAN Wenjiang, LI Tongchun, and DING Lin. Sensitivity analysis of reservoirs seepage discharge based on improved BP network[J]. Journal of China Three Gorges University (Natural Science), 2012, 34(6): 23-27.

WANG Lin, ZENG Yi, and CHEN Tao. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting[J]. Expert Systems with Applications, 2014, 42(2): 855-863. doi: 10.1016/j.eswa.2014. 08.018.

RUCK D W, ROGERS S K, and KABRISKY M. Feature selection using a multilayer perceptrons[J]. Journal of Neural Network Computing, 1990, 2(2): 40-48.

ZURADA J M, MALINOWSKI A, and USUI S. Perturbation method for deleting redundant inputs of perceptron networks[J]. Neurocomputing, 1997, 14(2): 177-193. doi: 10.1007/978-3-662-45652-1_35.

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