基于最佳小波包基的高光谱影像特征制图
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摘要
鉴于在时频局部化能力方面小波包变换优于小波变换,将高光谱影像像元光谱曲线作为1维信号并对其进行多尺度小波包变换分解,得到不同尺度上的低频和高频成分向量。根据不同地物像元光谱小波包分解最佳基有很大差异,而同一地物像元光谱小波包分解的前若干个最佳基完全相同的特点,提出一种基于前若干个最佳小波包基特征参量数组的分类特征参量和目标识别方法,并对AVIRIS影像中的特征如地物植被、水体、岩石及某些阴影等进行提取与制图。
The wavelet packet transformation has better time-frequency localization ability compared with the wavelet transformation. The pixel spectral curves of hyperspectral images as one dimension signals were decomposed by multi-scale wavelet packet transformations, and acquired different-scale component vectors in low and high frequency signals. According to the fact that the pixel spectra of different objects have different bases of best wavelet packet but the pixel spectra of same features are identical ones in the first some best wavelet packet groups, and by means of the wavelet packet decomposition of the spectral features of some objects such as vegetation, water, rock and shadow, a new target identification method has been probed for mapping features based on the composition of the characteristic parameters of the first several best wavelet packet bases for each pixel spectrum. The experiment results show that the features to vegetation, water, rock and shadow on AVIRIS image can be identified and mapped.
The wavelet packet transformation has better time-frequency localization ability compared with the wavelet transformation. The pixel spectral curves of hyperspectral images as one dimension signals were decomposed by multi-scale wavelet packet transformations, and acquired different-scale component vectors in low and high frequency signals. According to the fact that the pixel spectra of different objects have different bases of best wavelet packet but the pixel spectra of same features are identical ones in the first some best wavelet packet groups, and by means of the wavelet packet decomposition of the spectral features of some objects such as vegetation, water, rock and shadow, a new target identification method has been probed for mapping features based on the composition of the characteristic parameters of the first several best wavelet packet bases for each pixel spectrum. The experiment results show that the features to vegetation, water, rock and shadow on AVIRIS image can be identified and mapped.
引文
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[10] YANG Chao, XU Ze-yin, LU Yu-rong. An Application of Wavelet Package Analysis to the Signal Compression [J]. Geo-information Science, 2004, 6(2) : 92-96.(杨超,胥泽银,卢玉蓉．小波包分析在信号压缩中的应用[J]．地球信息科学,2004,6(2):92-96．)
[11] SUN Yu, FAN Wan-chun, XU Jin, et al. Seismic Signal Classification Based on Wavelet Packet Transform [J]. Nuclear Electronics & Detection Technology, 2005, 25 (1): 32-36．(孙煜,范万春,许进,等．基于小波包变换的地震事件分类[J]．核电子学与探测技术,2005,25(1): 32-36．)
[12] WANG Yun-song, LIN De-jie. Image De-noise Based on Wavelet Packet Analysis [J]. Computing Technology and Automation, 2005, 24(4): 99-101.(王云松,林德杰．基于小波包分析的图像去噪处理[J]．计算技术与自动化, 2005,24(4):99-101．)
[13] HANBAY D, TURKOGLU I, DEMIR Y,et al. Prediction of Wastewater Treatment Plant Performance Based on Wavelet Packet Decomposition and Neural Networks [J]. Expert Systems with Applications (2006), doi: 10. 1016/ j. eswa. 2006.10.030.
[14] WU Y, DU R. Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machining Processes[J]. Mechanical Systems and Signal Processing, 1996, 10(1): 29-53.
[2] CZAJA W. Characterizations of Cabor Systems via the Fourier Tran8form[J]. Collect Math, 2000, 51(2) : 205-224.
[3] AMATO U, ANGELINI C, SERIO C. Compression of AVHRR Images by Wavelet Packets [J]. Environmental Modeling & Software, 2000, 15: 127-138.
[4] WANG Xiang-yang, YANG Hong-ying. A New Wavelet Packet Coding Algorithm for Texture-rich Images [J]. Acta Geodaetica et Cartographica Sinica, 2004, 33(3) : 239-243.(王向阳,杨红颖．一种基于小波包变换的纹理图像压缩算法[J]．测绘学报,2004,33(3):239-243．)
[5] WU Chuan-qing, TONG Qing-xi, ZHENG Lan-fen. De-noise of Hyperspectral Image Based on Wavelet Transformation [J]. Remote Sensing Information, 2005, 4: 10-12.(吴传庆,童庆禧,郑兰芬．基于小波变换的高光谱图像消噪[J]．遥感信息,2005,4:10-12．)
[6] CHEN Ying, LIN Yi. Image Matching and Translation Based on Lifting Scheme Wavelet [J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(1):19-23.(陈鹰,林怡．基于提升小波的影像变换与匹配[J]．测绘学报,2006,35 (1):19-23．)
[7] KAEWPIJIT S. Automatic Reduction of Hyperspectral Imagery Using Wavelet Spectral Analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4): 863-871.
[8] LI Xin-shuang, ZHANG Liang-pei, LI Ping-xiang,et al. Hyperspectral Image Classification Based on Features of Wavelet Vectors[J]. Geomatics and Information Science of Wuhan U-niversity,2006, 31(3): 274-277.(李新双,张良培,李平湘,等．基于小波分量特征值匹配的高光谱影像分类[J]．武汉大学学报(信息科学版),2006,31(3):274-277．)
[9] LU Wei, YU Xu-chu, LIU Juan. An Adaptive Wavelet De-Noising on Hyperspectral Data[J]. Journal of Information Engineering University, 2005,6(2):91-95.(路威,余旭初．刘娟．高光谱遥感数据自适应小波滤噪[J]．信息工程大学学报,2005,6(2):91-95．)
[10] YANG Chao, XU Ze-yin, LU Yu-rong. An Application of Wavelet Package Analysis to the Signal Compression [J]. Geo-information Science, 2004, 6(2) : 92-96.(杨超,胥泽银,卢玉蓉．小波包分析在信号压缩中的应用[J]．地球信息科学,2004,6(2):92-96．)
[11] SUN Yu, FAN Wan-chun, XU Jin, et al. Seismic Signal Classification Based on Wavelet Packet Transform [J]. Nuclear Electronics & Detection Technology, 2005, 25 (1): 32-36．(孙煜,范万春,许进,等．基于小波包变换的地震事件分类[J]．核电子学与探测技术,2005,25(1): 32-36．)
[12] WANG Yun-song, LIN De-jie. Image De-noise Based on Wavelet Packet Analysis [J]. Computing Technology and Automation, 2005, 24(4): 99-101.(王云松,林德杰．基于小波包分析的图像去噪处理[J]．计算技术与自动化, 2005,24(4):99-101．)
[13] HANBAY D, TURKOGLU I, DEMIR Y,et al. Prediction of Wastewater Treatment Plant Performance Based on Wavelet Packet Decomposition and Neural Networks [J]. Expert Systems with Applications (2006), doi: 10. 1016/ j. eswa. 2006.10.030.
[14] WU Y, DU R. Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machining Processes[J]. Mechanical Systems and Signal Processing, 1996, 10(1): 29-53.
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