Classification of Pre-Filtered Multichannel Remote Sensing Images

Multichannel remote sensing (RS) has gained popularity and has been successfully applied for solving numerous practical tasks as forestry, agriculture, hydrology, meteorology, ecology, urban area and pollution control, etc. (Chang, 2007). Using the term “multichannel”, we mean a wide set of imaging approaches and RS systems (complexes) including multifrequency and dual/multi polarization radar (Oliver & Quegan, 2004), multiand hyperspectral optical and infrared sensors. While for such radars the number of formed images is a few, the number of channels (components or sub-bands) in images can be tens, hundreds and even more than one thousand for optical/infrared imagers. TerraSAR-X is a good example of modern multichannel radar system; AVIRIS, HYDICE, HYPERION and others can serve as examples of modern hyperspectral imagers, both airborne and spaceborne (Landgrebe, 2002; Schowengerdt, 2007).

[1]  Vladimir V. Lukin,et al.  Adaptive nonlinear vector filtering of multichannel radar images , 1997, Optics & Photonics.

[2]  Aleksandra Pizurica,et al.  Denoising of multicomponent images using wavelet least-squares estimators , 2008, Image Vis. Comput..

[3]  Hongqin Zhang,et al.  Hyperspectral image visualization based on a human visual model , 2008, Electronic Imaging.

[4]  Andrey A. Kurekin,et al.  Comparison of component and vector filter performance with application to multichannel and color image processing , 1999, NSIP.

[5]  Nikolay N. Ponomarenko,et al.  AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA , 2010 .

[6]  Aleksandra Pizurica,et al.  Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising , 2006, IEEE Transactions on Image Processing.

[7]  David A. Landgrebe,et al.  Partially supervised classification using weighted unsupervised clustering , 1999, IEEE Trans. Geosci. Remote. Sens..

[8]  Francesc Aulí Llinàs,et al.  Effects of JPEG and JPEG2000 Lossy Compression on Remote Sensing Image Classification for Mapping Crops and Forest Areas , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[9]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[10]  Jennifer L. Dungan,et al.  Estimation of signal-to-noise: a new procedure applied to AVIRIS data , 1989 .

[11]  Charles Kervrann,et al.  Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation , 2008, International Journal of Computer Vision.

[12]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[13]  Thomas L. Ainsworth,et al.  Classification comparisons between dual-pol and quad-pol SAR imagery , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Fionn Murtagh,et al.  Image restoration with noise suppression using a multiresolution support. , 1995 .

[15]  Salah Bourennane,et al.  Multiway Filtering Applied on Hyperspectral Images , 2006, ACIVS.

[16]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[17]  Antonio Plaza,et al.  Parallel Classification of Hyperspectral Images Using Neural Networks , 2008 .

[18]  Nikolay N. Ponomarenko,et al.  Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing , 2005, EURASIP J. Adv. Signal Process..

[19]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[20]  G. Eichmann,et al.  Vector median filters , 1987 .

[21]  Anne H. Schistad Solberg,et al.  A comparison of methods for improving classification of hyperspectral data , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Corinne Mailhes,et al.  Quality criteria benchmark for hyperspectral imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Begüm Demir,et al.  Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions , 2011, IEEE Geoscience and Remote Sensing Letters.

[24]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[25]  Peyman Milanfar,et al.  Is Denoising Dead? , 2010, IEEE Transactions on Image Processing.

[26]  K. Plataniotis,et al.  Color Image Processing and Applications , 2000 .

[27]  John P. Kerekes,et al.  Hyperspectral Imaging System Modeling , 2003 .

[28]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Andrei A. Kurekin,et al.  Modified Vector Sigma-Filter for the Processing of Multichannel Radar Images and Increasing Reliability of its Interpretation , 2002 .

[30]  Karen O. Egiazarian,et al.  Video denoising by sparse 3D transform-domain collaborative filtering , 2007, 2007 15th European Signal Processing Conference.

[31]  Vladimir V. Lukin,et al.  Methods for Blind Estimation of the Variance of Mixed Noise and Their Performance Analysis , 2011 .

[32]  Vladimir V. Lukin,et al.  Processing Multichannel Radar Images by Modified Vector Sigma Fukter FIR Edge Detectuib Ebgabcement , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[33]  Nikolay N. Ponomarenko,et al.  Efficiency analysis of color image filtering , 2011, EURASIP J. Adv. Signal Process..

[34]  V. Lukin,et al.  Potential MSE of color image local filtering in component-wise and vector cases , 2011, 2011 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM).

[35]  Nikolay N. Ponomarenko,et al.  Processing of Hyperspectral Imagery for Contamination Detection in Urban Areas , 2011 .

[36]  Florence Tupin,et al.  Patch similarity under non Gaussian noise , 2011, 2011 18th IEEE International Conference on Image Processing.

[37]  Nikolay N. Ponomarenko,et al.  FILTERING : POTENTIAL EFFICIENCY AND CURRENT PROBLEMS , 2011 .

[38]  A. Barducci,et al.  CHRIS-PROBA PERFORMANCE EVALUATION : SIGNAL-TO-NOISE RATIO , INSTRUMENT EFFICIENCY AND DATA QUALITY FROM ACQUISITIONS OVER SAN ROSSORE ( ITALY ) TEST SITE , 2005 .

[39]  Guangyi Chen,et al.  Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Nikolay N. Ponomarenko,et al.  Adaptive DCT-based filtering of images corrupted by spatially correlated noise , 2008, Electronic Imaging.

[41]  Nikolay N. Ponomarenko,et al.  Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics , 2011 .

[42]  Salah Bourennane,et al.  Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images , 2008, IEEE Geoscience and Remote Sensing Letters.

[43]  Layne T. Watson,et al.  An Adaptive Noise-Filtering Algorithm for AVIRIS Data With Implications for Classification Accuracy , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Stefano Pignatti,et al.  Experimental Approach to the Selection of the Components in the Minimum Noise Fraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Chein-I. Chang.  Hyperspectral Data Exploitation: Theory and Applications , 2007 .

[46]  Nikolay N. Ponomarenko,et al.  3D DCT Based Filtering of Color and Multichannel Images , 2008 .

[47]  Vladimir V. Lukin,et al.  Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images , 2011, IEEE Journal of Selected Topics in Signal Processing.

[48]  Nikolay N. Ponomarenko,et al.  HVS-metric-based performance analysis of image denoising algorithms , 2011, 3rd European Workshop on Visual Information Processing.

[49]  A. Barducci,et al.  Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers , 2006 .

[50]  Laurent Ferro-Famil,et al.  Classification de données SAR multifréquences polarimétriques , 2001, Ann. des Télécommunications.

[51]  J. Astola,et al.  ON BETWEEN-COEFFICIENT CONTRAST MASKING OF DCT BASIS FUNCTIONS , 2007 .

[52]  Alessandro Foi,et al.  Denoising of single-look SAR images based on variance stabilization and nonlocal filters , 2010, 2010 International Conference on Mathematical Methods in Electromagnetic Theory.

[53]  Josiane Zerubia,et al.  Texture feature analysis using a gauss-Markov model in hyperspectral image classification , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[54]  J. Shan,et al.  An Optimal Fusion Approach for Optical and SAR Images , 2006 .

[55]  Nikolay N. Ponomarenko,et al.  A method for blind estimation of spatially correlated noise characteristics , 2010, Electronic Imaging.

[57]  V. Lukin,et al.  Preliminary Automatic Analysis of Characteristics of Hypespectral Aviris Images , 2006, 2006 International Conference on Mathematical Methods in Electromagnetic Theory.

[58]  Jong-Sen Lee,et al.  Digital image smoothing and the sigma filter , 1983, Comput. Vis. Graph. Image Process..

[59]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[60]  Nikolay N. Ponomarenko,et al.  Classification of filtered multichannel images , 2010, Remote Sensing.

[61]  Oleksiy B. Pogrebnyak,et al.  Discrete cosine transform-based local adaptive filtering of images corrupted by nonstationary noise , 2010, J. Electronic Imaging.

[62]  S. Quegan,et al.  Understanding Synthetic Aperture Radar Images , 1998 .

[63]  Peyman Milanfar,et al.  Practical Bounds on Image Denoising: From Estimation to Information , 2011, IEEE Transactions on Image Processing.

[64]  Giuseppe Satalino,et al.  Comparison of polarimetric SAR observables in terms of classification performance , 2008 .