Signal Processing for Remote Sensing - Chapter 11 docx

... Transform 209 11. 3.3 The Projected PC Transform 209 11. 3.4 Evaluation of Compression Performance Using Two Different Metrics 210 11. 3.4.1 PC Filtering 210 11. 3.4.2 PC Regression 211 11.3.5 NAPC ... 207 11. 2.2 The Motivation for Computationall y Efficient Algorithms 208 11. 3 Principal Components Analysis of Hyperspec tral Sounding Data 208 11. 3.1 The PC Transform 209 11. 3.2...

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... to 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 1.5 2 2.5 3 3.5 4 False positive (a) ROC 3-D plot for vehicle (c) ROC 3-D plot for artillery (e) ROC 3-D plot for jet (f) ROC 3-D plot for shuttle (d) ROC 3-D plot for rocket (b) ROC 3-D plot for missile True positive True ... three-dimensional receiver operating characteristic (3-D ROC) curve for each one of the neural modules (see Figur...

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... (see Figure 11. 2). In signal- based fusion, signals from different sensors are combined to create a new signal with a better signal- to-noise ratio than the original signals [18]. Techniques for signal- level ... Rem. Sens., 37(3), 119 9–1203, 1999. C.H. Chen/Image Processing for Remote Sensing 66641_C 011 Final Proof page 269 3.9.2007 2:11pm Compositor Name: JGanesan Data...

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... beamforming for non-Gaussian signals, IEE Proc F, 140(6), 362, 1993. 13. Comon, P., Independent component analysis, a new concept? Signal Processing, 36(3), 287, 1994. 14. De Lathauwer, L., Signal ... for wavefield separation and enhancement of the signal- to-noise ratio (SNR) in the data set. The use of multi-linear methods such as the HOSVD is motivated by the natural modeling of...

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... Seismic Signal Processing 109 6.3.1 Marmousi Data Set 109 6.3.2 Velocity Analysis, NMO Correction, and Stacking 110 6.3.3 The Advantage of Stacking 112 6.3.4 Factor Analysis vs. Stacking 112 6.3.5 ... Factor Analysis 114 6.3.5.1 Factor Analysis Scheme No. 1 114 6.3.5.2 Factor Analysis Scheme No. 2 114 6.3.5.3 Factor Analysis Scheme No. 3 116 6.3.5.4 Factor Analysis Scheme No...

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... man y analyse s if not proper ly correc ted. For ex- ampl e, if this TOPS AR DEM is used for to pograp hy red uction in repeat-pas s InSA R usin g ERS-2 data with a perpen dicular baseline of about ... a ß 2007 by Taylor & Francis Group, LLC. for each 9-by-9 pixel group, it is equivalent to simply filling the regions of missing data with 9- by-9 identical v alues t hat are the s...

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... bibliographical references and index. ISBN-13: 97 8-1 -4 20 0-6 66 4-7 ISBN-10: 1-4 20 0-6 66 4-1 1. Remote sensing Data processing. 2. Image processing. I. Chen, C.H. (Chi-hau), 193 7- II. Title. G70.4.I44 2008 621.36’78 ... Aksoy 11. Data Fusion for Remote- Sensing Applications 249 Anne H.S. Solberg 12. The Hermite Transform: An Efficient Tool for Noise Reduc...

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... of random forests for classification of multi-source remote sensing data and hyperspectral remote sensing data has been discussed. Random forests should be con- sidered attractive for classification ... informative when compared to CART-like trees. C.H. Chen/Image Processing for Remote Sensing 66641_C003 Final Proof page 64 3.9.2007 2:03pm Compositor Name: JGanesan 64 Imag...

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... (right). 1 543 26 789 1 111 21 122 C.H. Chen/Image Processing for Remote Sensing 66641_C004 Final Proof page 90 3.9.2007 2:04pm Compositor Name: JGanesan 90 Image Processing for Remote Sensing © 2008 ... AdaBoost with multi-class labels is mentioned. C.H. Chen/Image Processing for Remote Sensing 66641_C004 Final Proof page 80 3.9.2007 2:04pm Compositor Name: JGanesan...

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... multi-temporal images, multi-temporal ground-truth information is also needed. This information is used for identifying, for each possible land-cover class, spectral signature samples for performing supervised ... Image Processing for Remote Sensing © 2008 by Taylor & Francis Group, LLC variations occurring on the ground by analyzing multi-temporal remote- sensing images...

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