|
- 2016
利用小波核最小噪声分离进行高光谱影像SVM分类
|
Abstract:
针对高光谱遥感影像线性特征提取方法在一定程度上会降低地物类别的可分性问题,在最小噪声分离变换基础上引入核方法,以小波核函数代替传统核函数,并将新型核最小噪声分离方法与支持向量机方法相结合,对高光谱影像数据进行分类。实验结果表明,基于小波核最小噪声分离变换的方法适合于高光谱遥感影像的非线性特征,将其应用于HYDICE系统与AVIRIS系统所获得的实验数据集,与对照算法相比,总体分类精度可提高3%~9%
[1] | Wang Yiting, Huang Shiqi, Liu Daizhi, et al. Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images[J]. <em>Remote Sensing, Environment and Transportation Engineering</em>,2012(1):1-4 |
[2] | Dopido I, Villa A, Plaza A, et al. A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification[J]. <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, 2012, 5(2):421-435 |
[3] | Goetz A F H. Three Decades of Hyperspectral Remote Sensing of the Earth:A Personal View[J]. <em>Remote Sensing of Environment</em>, 2009, 113(1):5-16 |
[4] | Plaza A. Recent Advances in Techniques for Hyperspectral Image Processing[J]. <em>Remote Sensing of Environment</em>, 2009, 113(1):110-122 |
[5] | Bioucas-Dias J M, Plaza A, Camps-Valls G, et al. Hyperspectral Remote Sensing Data Analysis and Future Challenges[J]. <em>Geoscience and Remote Sensing Magazine</em>, 2013(2):6-36 |
[6] | Fauvel M, Chanussot J, Benediktsson J A. A Spatial spectral Kernel Based Approach for the Classification of Remote sensing Images[J]. <em>Pattern Recognition</em>, 2012,45:381-392 |
[7] | Zhao Chunhui, Zhang Yi, Wang Yulei. Relevant Vector Machine Classification of Hyperspectral Image Based on Wavelet Kernel Principal Component Analysis[J]. <em>Journal of Electronics & Information Technology</em>, 2012,34(8):1905-1910(赵春晖,张焱,王玉磊. 基于小波核主成分分析的相关向量机高光谱图像分类[J]. 电子与信息学报,2012,34(8):1905-1910) |
[8] | Wan Jiaqiang, Wang Yue, Liu Yu. Improvement of KPCA on Feature Extraction of Classification Data[J]. <em>Computer Engineering and Design</em>, 2010,31(18):4085-4088(万家强,王越,刘羽.改进KPCA对分类数据的特征提取[J]. 计算机工程与设计,2010,31(18):4085-4088) |
[9] | Chen J, Wang C,Wang R. Using Stacked Generalization to Combine SVMs in Magnitude and Shape Feature Spaces for Classification of Hyperspectral Date[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2009, 47(7):2193-2205 |
[10] | Hosseini S A, Ghassemian H. A New Fast Algorithm for Multiclass Hyperspectral Image Classification with SVM[J]. <em>International Journal of Remote Sensing</em>, 2011, 32(23):8657-8683 |
[11] | Chen Yi, Nasser M, Trac D T. Hyperspectral Image Classification via Kernel Sparse Representation[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2013,51(1):217-231 |
[12] | Lin Na, Yang Wunian, Wang Bin. Hyperspecral Image Feature Extraction via Kernel Minimum Noise Fraction Transform[J]. <em>Geomatics and Information Science of Wuhan University</em>,2013,38(8):988-992(林娜,杨武年,王斌. 高光谱遥感影像核最小噪声分离变换特征提取[J]. 武汉大学学报\5信息科学版,2013,38(8):988-992) |
[13] | Wu Fangfang, Zhao Yinliang. Novel Reduced Support Vector Machine on Morlet Wavelet Kernel Function[J]. <em>Control and Decision</em>, 2006,21(8):848-856(武方方,赵银亮. 一种基于Morlet小波核的约简支持向量机[J]. 控制与决策, 2006, 21(8):848-856) |
[14] | Yang Guopeng, Yu Xuchu, Zhou Xin, et al. Hyperspectral Image Feature Extraction Based on Generalized Discriminant Analysis[J].<em>Journal of Dalian Maritime University</em>, 2008,34(8):59-63(杨国鹏,余旭初,周欣,等.基于广义判别分析的高光谱影像特征提取[J].大连海事大学学报,2008,34(3):59-63) |