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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%。
  • 杜培軍, 譚琨, 夏俊士. 高光譜遙感影像分類與支持向量機應(yīng)用研究[M]. 北京: 科學(xué)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.
    童慶禧, 張兵, 鄭蘭芬. 高光譜遙感原理、技術(shù)及應(yīng)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.
    吳倩, 張榮, 徐大衛(wèi). 基于稀疏表示的高光譜數(shù)據(jù)壓縮算法[J]. 電子與信息學(xué)報, 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, 肖鴻, 劉興鵬, 等. BP神經(jīng)網(wǎng)絡(luò)在異向介質(zhì)基本結(jié)構(gòu)分析中的應(yīng)用[J]. 電子與信息學(xué)報, 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.
    張兵, 高連如. 高光譜圖像分類與目標(biāo)探測[M]. 北京: 科學(xué)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, 邢巖, 胡丹. 敏感性分析綜述[J]. 北京師范大學(xué)學(xué)報(自然科學(xué)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.
    張軍, 劉祖強, 張正祿, 等. 基于神經(jīng)網(wǎng)絡(luò)和模糊評判的滑坡敏感性分析[J]. 測繪科學(xué), 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.
    錢文江, 李同春, 丁林. 基于改進BP神經(jīng)網(wǎng)絡(luò)的庫區(qū)滲漏量敏感性分析[J]. 三峽大學(xué)學(xué)報(自然科學(xué)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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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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