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神經(jīng)網(wǎng)絡(luò)敏感性分析的高光譜遙感影像降維與分類方法

高紅民,  李臣明,  周惠,  張振,  陳玲慧,  何振宇

高紅民, 李臣明, 周惠, 張振, 陳玲慧, 何振宇. 神經(jīng)網(wǎng)絡(luò)敏感性分析的高光譜遙感影像降維與分類方法[J]. 電子與信息學(xué)報, 2016, 38(11): 2715-2723. doi: 10.11999/JEIT160052
引用本文: 高紅民, 李臣明, 周惠, 張振, 陳玲慧, 何振宇. 神經(jīng)網(wǎng)絡(luò)敏感性分析的高光譜遙感影像降維與分類方法[J]. 電子與信息學(xué)報, 2016, 38(11): 2715-2723. doi: 10.11999/JEIT160052
GAO Hongmin, LI Chenming, ZHOU Hui, ZHANG Zhen, CHEN Linghui, HE Zhenyu. Dimension Reduction and Classification of Hyperspectral Remote Sensing Images Based on Sensitivity Analysis of Artificial Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2715-2723. doi: 10.11999/JEIT160052
Citation: GAO Hongmin, LI Chenming, ZHOU Hui, ZHANG Zhen, CHEN Linghui, HE Zhenyu. Dimension Reduction and Classification of Hyperspectral Remote Sensing Images Based on Sensitivity Analysis of Artificial Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2715-2723. doi: 10.11999/JEIT160052

神經(jīng)網(wǎng)絡(luò)敏感性分析的高光譜遙感影像降維與分類方法

doi: 10.11999/JEIT160052
基金項目: 

中央高?；究蒲袠I(yè)務(wù)費項目(2014B13214, 2015B 26914)，十二五國家科技支撐計劃項目(2015BAB07B03)，河海大學(xué)國家級大學(xué)生創(chuàng)新訓(xùn)練計劃項目(201610294061)

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: 高光譜遙感影像由于其巨大的波段數(shù)直接導(dǎo)致信息的高冗余和數(shù)據(jù)處理的復(fù)雜，這不僅帶來龐大的計算量，而且會損害分類精度。因此，在對高光譜影像進行處理、分析之前進行降維變得非常必要。神經(jīng)網(wǎng)絡(luò)敏感性分析可以用于對模型的簡化降維，該文將該方法運用于高光譜遙感影像降維中，通過子空間劃分弱化波段之間的相關(guān)性，利用差分進化算法(DE)優(yōu)化神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)，采用Ruck敏感性分析方法剔除掉對分類貢獻較小的波段，從而實現(xiàn)降維。最后，采用AVIRIS影像進行實驗，所提算法相比其他相近的降維與分類方法能獲得更高的分類精度，達到85.83%，比其他相近方法中最優(yōu)方法高出0.31%。
    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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出版歷程
  • 收稿日期:  2016-01-13
  • 修回日期:  2016-06-08
  • 刊出日期:  2016-11-19

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