DIMENSION REDUCTION FOR HYPERSPECTRAL DATA USING RANDOMIZED PCA AND LAPLACIAN EIGENMAPS
|
|
- Claude Blankenship
- 6 years ago
- Views:
Transcription
1 DIMENSION REDUCTION FOR HYPERSPECTRAL DATA USING RANDOMIZED PCA AND LAPLACIAN EIGENMAPS YIRAN LI APPLIED MATHEMATICS, STATISTICS AND SCIENTIFIC COMPUTING ADVISOR: DR. WOJTEK CZAJA, DR. JOHN BENEDETTO DEPARTMENT OF MATHEMATICS UNIVERSITY OF MARYLAND, COLLEGE PARK
2 BACKGROUND: HYPERSPECTRAL IMAGING Light is described in terms of its wavelength A reflectance spectrum shows the reflectance of a material measured across a range of wavelengths. It helps identify certain materials uniquely We measure reflectance at many narrow, closely spaced wavelength bands When a spectrometer is used in an imaging sensor, the resulting images record a reflectance spectrum for each pixel in the images (Shippert, 2003)
3 SPECTRUM AND HYPERSPECTRAL IMAGERY Left: Reflectance spectra measured by laboratory spectrometers for three materials: a green bay laurel leaf, the mineral talc, and a silty loam soil. Right: The concept of hyperspectral imagery. (Shippert, 2003)
4 MULTISPECTRAL VS HYPERSPECTRAL Multispectral imaging measures reflectance at discrete and somewhat narrow bands. Multispectral images do not produce the "spectrum" of an object Hyperspectral deals with imaging narrow spectral bands over a continuous spectral range, and produce the spectra of all pixels in the scene. So a sensor with only 20 bands can also be hyperspectral when it covers the range from 500 to 700 nm with 20 bands each 10 nm wide. (Wikipedia: hyperspectral imaging)
5 AN EXAMPLE: SALINAS VALLEY, CALIFORNIA Left: sample band collected by 224-band sensor. It includes vegetables, bare soils, and vineyard fields. Right: Groundtruth of Salinas dataset (16 classes) (IC: Hyperspectral Remote Sensing Scenes)
6 PROBLEM Hyperspectral images are three dimensional (x-coordinate, y-coordinate, b) Each pixel has a different spectrum that represents different materials Sometimes over 100 bands and with large number of pixels Dimension reduction reduces the number of bands of a hyperspectral image It maps dimensional data into a lower dimension while preserving the main features of the original data. (hyperspectral imaging, Wikipedia)
7 PROJECT GOAL Reduce dimensionality of hyperspectral imaging Compare two algorithms to be implemented
8 METHODS Existing methods (partial) : Principal Component Analysis( PCA) Local Linear Embedding My Methods: Randomized PCA Laplacian Eigenmaps Neighborhood Preserving Embedding Classical multidimensional scaling Isomap Stochastic Proximity Embedding (Delft University)
9 COMPARISON BETWEEN TWO ALGORITHMS Compare two algorithms, Randomized PCA and Laplacian Eigenmaps, in terms of: Implementation Running time Results Difficulties during implementation
10 ALGORITHM 1: LAPLACIAN EIGENMAPS Consider the problem of mapping the weighted graph G to a line so that connected points stay as close together as possible, let y = y 1, y 2, y n T be such a map. Our goal is to minimize i,j y i y j 2 Wij Since i,j y i y j 2 Wij = 2y T Ly, the problem of finding argmin y T Ly given that y T Dy = 1, y T D1 = 0 becomes the minimum eigenvalue problem: Lf = λdf (Belkin, Niyogi, 2002)
11 ALGORITHM 1: THE ALGORITHM Step 1: Constructing the Adjacency Graph Construct a weighted graph with n nodes (n number of data points), and a set of edges connecting neighboring points. A) ε neighborhood: connected if B) n nearest neighbors Step 2: Choosing the weights A) Heat Kernel: x i x j 2 < ε W ij = e x i x j t 2 B) Simple Minded: W ij = 1 if connected and W ij = 0 otherwise
12 Step 3: Compute eigenvalues and eigenvectors for the generalized eigenvector problem: Lf = λdf (1) Where W is the weight matrix defined earlier, D is diagonal weight matrix, with D ii = j W ji, and L = D W Let f 0, f 1,, f n 1 be the solutions of equation (1), ordered such that 0 = λ 0 λ 1 λ n 1 Then the first m eigenvectors (excluding f 0 ), {f 1, f 2,, f m } are the desired vectors for embedding in m-dimensional Euclidean space (Belkin, Niyogi, 2002)
13 ALGORITHM 2: RANDOMIZED PCA INTRODUCTION Canonical construction of the best possible rank-k approximation to a real m n matrix A uses singular value decomposition (SVD) of A, A = UΣV T, Where U real unitary m m matrix, V is real unitary n n matrix, and Σ is real m n diagonal matrix with nonnegative, non increasing diagonal entries Best Approximation of A: A U Σ V T, Where U leftmost m k block of U, Σ k k upper left block of Σ, V leftmost n k block of V (Rokhlin, Szlam, Tygert, 2009)
14 Best because it minimizes the spectral norm A B for a rank-k matrix B = U Σ V T. In fact, A U Σ V T = σ k+1, Where σ k+1 is the k + 1 th greatest singular value Randomized PCA generates B such that A B Cm 1 4i+2 σk+1 with high probability ( ), where i is specified by user, and C depends on parameters of algorithm (Rokhlin, Szlam, Tygert, 2009)
15 ALGORITHM 2: THE ALGORITHM Choose l > k such that l m k Step 1: Generate a real l m matrix G whose entries are i.i.d normal Gaussian random variables, compute R = G AA T i A Step 2: Using SVD, form a real n k matrix Q whose columns are orthonormal, such that QS R T ρ k+1 for some k l matrix S, where ρ k+1 is the k + 1 th greatest singular value of R
16 Step 3: Compute T = AQ Step 4: Form an SVD of T: T = UΣW T, where U is a real m k matrix whose columns are orthonormal, W is a real k k matrix whose columns are orthonormal, Σ is a real diagonal k k matrix with nonnegative diagonal entries Step 5: Compute V = QW In this way, we get U, Σ, V as desired, and B = UΣV T (Rokhlin, Szlam, Tygert, 2009)
17 IMPLEMENTATION Hardware: Personal laptop/computers in the math computer lab Software: Matlab Database: 12 Band Moderate Dimension Image: June 1966 aircraft scanner Flightline C1 (Portion of Southern Tippecanoe County, Indiana) 220 Band Hyperspectral Image: June 12, 1992 AVIRIS image Indian Pine Test Site 3 (2 x 2 mile portion of Northwest Tippecanoe County, Indiana) 220 Band Hyperspectral Image: June 12, 1992 AVIRIS image North-South flight line (25 x 6 mile portion of Northwest Tippecanoe County, Indiana) Hyperspectral data from Norbert Weiner Center Data can be large (with 10,000^2 pixels, 200 bands, for example)
18 VALIDATION METHODS Delft University has developed Matlab toolbox for dimension reduction, which includes many methods, and is publically available Use algorithms from DR matlab toolbox to run on the same data and compare results For randomized PCA, check error bound: A B Cm 1 4i+2 σk+1 (Rohklin, 2009) Compare with ground truth images for the test cases
19 TEST PROBLEMS FOR VERIFICATION Test on known data set (as provided earlier), and compare results with ground truth classifications and images Test on smaller scales at first, and then move to large data set
20 EXPECTED RESULTS/CONCLUDING REMARKS Laplacian Eigenmaps should be easier to implement, but may take longer to run because it deals with solving the eigenvalue problem of large matrices Randomized PCA will be more difficult to implement, but will give desired results under unfavorable conditions with reasonable speed, and it should perform better than Laplacian eigenmaps when dealing with very large matrices
21 TIMELINE/MILESTONES October 17th: Project proposal Now to November, 2014: Implement and test laplacian eigenmaps, prepare for implementation of randomized PCA December, 2014: Midyear report and presentation January to March: Implement and test randomized PCA, compare two methods in various situations April to May: Final presentation and Final report
22 DELIVERABLES Presentation of data sets with reduced dimensions of both algorithms Comparison charts in terms of running time and accuracy of two different methods Comparison charts with other methods that are available from the DR matlab toolbox Data sets, Matlab codes, presentations, proposals, mid-year report, final report
23 BIBLIOGRAPHY Shippert, Peg. Introduction to Hyperspectral Image Analysis. Online Journal of Space Communication, issue No. 3: Remote Sensing of Earth via Satellite. Winter Hyperspectral Imaging. From Wikipedia. Oct. 6 th, Belkin, Mikhail; Niyogi, Partha. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, vol 15. Dec. 8 th, Web.
24 Rokhlin, Vladimir; Szlam, Arthur; Tygert, Mark. A Randomized Algorithm for Principal Component Analysis. SIAM Journal on Matrix Analysis and Applications Volume 31 Issue 3. August Web. ftp://ftp.math.ucla.edu/pub/camreport/cam08-60.pdf Matlab Toolbox for Dimension Reduction. Delft University. Web. Oct. 6 th, ction.html IC: Hyperspectral Remote Sensing Scenes. Web. Oct. 6 th, Scenes
25 Hyperspectral Images. Web. Oct. 6 th,
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis Yiran Li yl534@math.umd.edu Advisor: Wojtek Czaja wojtek@math.umd.edu 10/17/2014 Abstract
More informationDimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis:midyear Report
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis:midyear Report Yiran Li yl534@math.umd.edu Advisor: Wojtek Czaja wojtek@math.umd.edu
More informationLinear and Non-linear Dimentionality Reduction Applied to Gene Expression Data of Cancer Tissue Samples
Linear and Non-linear Dimentionality Reduction Applied to Gene Expression Data of Cancer Tissue Samples Franck Olivier Ndjakou Njeunje Applied Mathematics, Statistics, and Scientific Computation University
More informationFrame based kernel methods for hyperspectral imagery data
Frame based kernel methods for hyperspectral imagery data Norbert Wiener Center Department of Mathematics University of Maryland, College Park Recent Advances in Harmonic Analysis and Elliptic Partial
More informationSchroedinger Eigenmaps with Nondiagonal Potentials for Spatial-Spectral Clustering of Hyperspectral Imagery
Schroedinger Eigenmaps with Nondiagonal Potentials for Spatial-Spectral Clustering of Hyperspectral Imagery Nathan D. Cahill a, Wojciech Czaja b, and David W. Messinger c a Center for Applied and Computational
More informationSpatial-Spectral Dimensionality Reduction of Hyperspectral Imagery with Partial Knowledge of Class Labels
Spatial-Spectral Dimensionality Reduction of Hyperspectral Imagery with Partial Knowledge of Class Labels Nathan D. Cahill, Selene E. Chew, and Paul S. Wenger Center for Applied and Computational Mathematics,
More informationUnsupervised Clustering of Bitcoin Transaction Data
Unsupervised Clustering of Bitcoin Transaction Data Midyear Report 1 AMSC 663/664 Project Advisor: Dr. Chris Armao By: Stefan Poikonen Bitcoin: A Brief Refresher 2 Bitcoin is a decentralized cryptocurrency
More informationLarge-Scale Face Manifold Learning
Large-Scale Face Manifold Learning Sanjiv Kumar Google Research New York, NY * Joint work with A. Talwalkar, H. Rowley and M. Mohri 1 Face Manifold Learning 50 x 50 pixel faces R 2500 50 x 50 pixel random
More informationLocality Preserving Projections (LPP) Abstract
Locality Preserving Projections (LPP) Xiaofei He Partha Niyogi Computer Science Department Computer Science Department The University of Chicago The University of Chicago Chicago, IL 60615 Chicago, IL
More informationHYPERSPECTRAL REMOTE SENSING
HYPERSPECTRAL REMOTE SENSING By Samuel Rosario Overview The Electromagnetic Spectrum Radiation Types MSI vs HIS Sensors Applications Image Analysis Software Feature Extraction Information Extraction 1
More informationLocality Preserving Projections (LPP) Abstract
Locality Preserving Projections (LPP) Xiaofei He Partha Niyogi Computer Science Department Computer Science Department The University of Chicago The University of Chicago Chicago, IL 60615 Chicago, IL
More informationCOMPRESSED DETECTION VIA MANIFOLD LEARNING. Hyun Jeong Cho, Kuang-Hung Liu, Jae Young Park. { zzon, khliu, jaeypark
COMPRESSED DETECTION VIA MANIFOLD LEARNING Hyun Jeong Cho, Kuang-Hung Liu, Jae Young Park Email : { zzon, khliu, jaeypark } @umich.edu 1. INTRODUCTION In many imaging applications such as Computed Tomography
More informationSpectral Clustering on Handwritten Digits Database
October 6, 2015 Spectral Clustering on Handwritten Digits Database Danielle dmiddle1@math.umd.edu Advisor: Kasso Okoudjou kasso@umd.edu Department of Mathematics University of Maryland- College Park Advance
More informationLocally Linear Landmarks for large-scale manifold learning
Locally Linear Landmarks for large-scale manifold learning Max Vladymyrov and Miguel Á. Carreira-Perpiñán Electrical Engineering and Computer Science University of California, Merced http://eecs.ucmerced.edu
More informationImage Similarities for Learning Video Manifolds. Selen Atasoy MICCAI 2011 Tutorial
Image Similarities for Learning Video Manifolds Selen Atasoy MICCAI 2011 Tutorial Image Spaces Image Manifolds Tenenbaum2000 Roweis2000 Tenenbaum2000 [Tenenbaum2000: J. B. Tenenbaum, V. Silva, J. C. Langford:
More informationApplication of Spectral Clustering Algorithm
1/27 Application of Spectral Clustering Algorithm Danielle Middlebrooks dmiddle1@math.umd.edu Advisor: Kasso Okoudjou kasso@umd.edu Department of Mathematics University of Maryland- College Park Advance
More informationLow-dimensional Representations of Hyperspectral Data for Use in CRF-based Classification
Rochester Institute of Technology RIT Scholar Works Presentations and other scholarship 8-31-2015 Low-dimensional Representations of Hyperspectral Data for Use in CRF-based Classification Yang Hu Nathan
More informationRemote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding (CSSLE)
2016 International Conference on Artificial Intelligence and Computer Science (AICS 2016) ISBN: 978-1-60595-411-0 Remote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding
More informationFeature selection. Term 2011/2012 LSI - FIB. Javier Béjar cbea (LSI - FIB) Feature selection Term 2011/ / 22
Feature selection Javier Béjar cbea LSI - FIB Term 2011/2012 Javier Béjar cbea (LSI - FIB) Feature selection Term 2011/2012 1 / 22 Outline 1 Dimensionality reduction 2 Projections 3 Attribute selection
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spring 2014 TTh 14:30-15:45 CBC C313 Lecture 06 Image Structures 13/02/06 http://www.ee.unlv.edu/~b1morris/ecg782/
More informationSemi-Supervised Normalized Embeddings for Fusion and Land-Use Classification of Multiple View Data
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 12-2018 Semi-Supervised Normalized Embeddings for Fusion and Land-Use Classification of Multiple View Data Poppy
More informationMultidimensional scaling Based in part on slides from textbook, slides of Susan Holmes. October 10, Statistics 202: Data Mining
Multidimensional scaling Based in part on slides from textbook, slides of Susan Holmes October 10, 2012 1 / 1 Multidimensional scaling A visual tool Recall the PCA scores were X V = U where X = HX S 1/2
More informationData fusion and multi-cue data matching using diffusion maps
Data fusion and multi-cue data matching using diffusion maps Stéphane Lafon Collaborators: Raphy Coifman, Andreas Glaser, Yosi Keller, Steven Zucker (Yale University) Part of this work was supported by
More informationCOSC160: Detection and Classification. Jeremy Bolton, PhD Assistant Teaching Professor
COSC160: Detection and Classification Jeremy Bolton, PhD Assistant Teaching Professor Outline I. Problem I. Strategies II. Features for training III. Using spatial information? IV. Reducing dimensionality
More informationAvailable online at ScienceDirect. Procedia Computer Science 93 (2016 )
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 93 (2016 ) 396 402 6th International Conference On Advances In Computing & Communications, ICACC 2016, 6-8 September 2016,
More informationSpectral Clustering X I AO ZE N G + E L HA M TA BA S SI CS E CL A S S P R ESENTATION MA RCH 1 6,
Spectral Clustering XIAO ZENG + ELHAM TABASSI CSE 902 CLASS PRESENTATION MARCH 16, 2017 1 Presentation based on 1. Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4
More informationA MAXIMUM NOISE FRACTION TRANSFORM BASED ON A SENSOR NOISE MODEL FOR HYPERSPECTRAL DATA. Naoto Yokoya 1 and Akira Iwasaki 2
A MAXIMUM NOISE FRACTION TRANSFORM BASED ON A SENSOR NOISE MODEL FOR HYPERSPECTRAL DATA Naoto Yokoya 1 and Akira Iwasaki 1 Graduate Student, Department of Aeronautics and Astronautics, The University of
More informationSpectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification Using Markov Random Fields
Rochester Institute of Technology RIT Scholar Works Presentations and other scholarship 7-31-2016 Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification Using Markov
More informationHyperspectral Remote Sensing
Hyperspectral Remote Sensing Multi-spectral: Several comparatively wide spectral bands Hyperspectral: Many (could be hundreds) very narrow spectral bands GEOG 4110/5100 30 AVIRIS: Airborne Visible/Infrared
More informationHyperspectral image segmentation using spatial-spectral graphs
Hyperspectral image segmentation using spatial-spectral graphs David B. Gillis* and Jeffrey H. Bowles Naval Research Laboratory, Remote Sensing Division, Washington, DC 20375 ABSTRACT Spectral graph theory
More informationAdvanced Machine Learning Practical 2: Manifold Learning + Clustering (Spectral Clustering and Kernel K-Means)
Advanced Machine Learning Practical : Manifold Learning + Clustering (Spectral Clustering and Kernel K-Means) Professor: Aude Billard Assistants: Nadia Figueroa, Ilaria Lauzana and Brice Platerrier E-mails:
More informationLand Mapping Based On Hyperspectral Image Feature Extraction Using Guided Filter
Land Mapping Based On Hyperspectral Image Feature Extraction Using Guided Filter Fazeela Hamza 1, Sreeram S 2 1M.Tech Student, Dept. of Computer Science & Engineering, MEA Engineering College, Perinthalmanna,
More informationUnsupervised Learning
Unsupervised Learning Learning without Class Labels (or correct outputs) Density Estimation Learn P(X) given training data for X Clustering Partition data into clusters Dimensionality Reduction Discover
More informationGeneral Instructions. Questions
CS246: Mining Massive Data Sets Winter 2018 Problem Set 2 Due 11:59pm February 8, 2018 Only one late period is allowed for this homework (11:59pm 2/13). General Instructions Submission instructions: These
More informationABSTRACT. Professor John Benedetto Department of Mathematics. Professor Wojciech Czaja Department of Mathematics
ABSTRACT Title of dissertation: Dimensionality Reduction for Hyperspectral Data David P. Widemann, Doctor of Philosophy, 2008 Dissertation directed by: Professor John Benedetto Department of Mathematics
More informationRecognition, SVD, and PCA
Recognition, SVD, and PCA Recognition Suppose you want to find a face in an image One possibility: look for something that looks sort of like a face (oval, dark band near top, dark band near bottom) Another
More informationThe Analysis of Parameters t and k of LPP on Several Famous Face Databases
The Analysis of Parameters t and k of LPP on Several Famous Face Databases Sujing Wang, Na Zhang, Mingfang Sun, and Chunguang Zhou College of Computer Science and Technology, Jilin University, Changchun
More informationCSE 6242 A / CS 4803 DVA. Feb 12, Dimension Reduction. Guest Lecturer: Jaegul Choo
CSE 6242 A / CS 4803 DVA Feb 12, 2013 Dimension Reduction Guest Lecturer: Jaegul Choo CSE 6242 A / CS 4803 DVA Feb 12, 2013 Dimension Reduction Guest Lecturer: Jaegul Choo Data is Too Big To Do Something..
More informationFace Recognition using Laplacianfaces
Journal homepage: www.mjret.in ISSN:2348-6953 Kunal kawale Face Recognition using Laplacianfaces Chinmay Gadgil Mohanish Khunte Ajinkya Bhuruk Prof. Ranjana M.Kedar Abstract Security of a system is an
More informationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 13, NO. 8, AUGUST
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 13, NO. 8, AUGUST 2016 1059 A Modified Locality-Preserving Projection Approach for Hyperspectral Image Classification Yongguang Zhai, Lifu Zhang, Senior
More informationCluster Analysis (b) Lijun Zhang
Cluster Analysis (b) Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Grid-Based and Density-Based Algorithms Graph-Based Algorithms Non-negative Matrix Factorization Cluster Validation Summary
More informationDimensionality Reduction using Hybrid Support Vector Machine and Discriminant Independent Component Analysis for Hyperspectral Image
Dimensionality Reduction using Hybrid Support Vector Machine and Discriminant Independent Component Analysis for Hyperspectral Image Murinto 1, Nur Rochmah Dyah PA 2 1,2 Department of Informatics Engineering
More informationUnsupervised learning in Vision
Chapter 7 Unsupervised learning in Vision The fields of Computer Vision and Machine Learning complement each other in a very natural way: the aim of the former is to extract useful information from visual
More informationStratified Structure of Laplacian Eigenmaps Embedding
Stratified Structure of Laplacian Eigenmaps Embedding Abstract We construct a locality preserving weight matrix for Laplacian eigenmaps algorithm used in dimension reduction. Our point cloud data is sampled
More informationComputational color Lecture 1. Ville Heikkinen
Computational color Lecture 1 Ville Heikkinen 1. Introduction - Course context - Application examples (UEF research) 2 Course Standard lecture course: - 2 lectures per week (see schedule from Weboodi)
More informationClassification of Hyperspectral Breast Images for Cancer Detection. Sander Parawira December 4, 2009
1 Introduction Classification of Hyperspectral Breast Images for Cancer Detection Sander Parawira December 4, 2009 parawira@stanford.edu In 2009 approximately one out of eight women has breast cancer.
More informationGRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION
GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION Nasehe Jamshidpour a, Saeid Homayouni b, Abdolreza Safari a a Dept. of Geomatics Engineering, College of Engineering,
More informationSpectral Classification
Spectral Classification Spectral Classification Supervised versus Unsupervised Classification n Unsupervised Classes are determined by the computer. Also referred to as clustering n Supervised Classes
More informationNonlinear projections. Motivation. High-dimensional. data are. Perceptron) ) or RBFN. Multi-Layer. Example: : MLP (Multi(
Nonlinear projections Université catholique de Louvain (Belgium) Machine Learning Group http://www.dice.ucl ucl.ac.be/.ac.be/mlg/ 1 Motivation High-dimensional data are difficult to represent difficult
More informationIsometric Mapping Hashing
Isometric Mapping Hashing Yanzhen Liu, Xiao Bai, Haichuan Yang, Zhou Jun, and Zhihong Zhang Springer-Verlag, Computer Science Editorial, Tiergartenstr. 7, 692 Heidelberg, Germany {alfred.hofmann,ursula.barth,ingrid.haas,frank.holzwarth,
More informationLab # 2 - ACS I Part I - DATA COMPRESSION in IMAGE PROCESSING using SVD
Lab # 2 - ACS I Part I - DATA COMPRESSION in IMAGE PROCESSING using SVD Goals. The goal of the first part of this lab is to demonstrate how the SVD can be used to remove redundancies in data; in this example
More informationRobust Pose Estimation using the SwissRanger SR-3000 Camera
Robust Pose Estimation using the SwissRanger SR- Camera Sigurjón Árni Guðmundsson, Rasmus Larsen and Bjarne K. Ersbøll Technical University of Denmark, Informatics and Mathematical Modelling. Building,
More informationInternational Journal of Advancements in Research & Technology, Volume 2, Issue 8, August ISSN
International Journal of Advancements in Research & Technology, Volume 2, Issue 8, August-2013 244 Image Compression using Singular Value Decomposition Miss Samruddhi Kahu Ms. Reena Rahate Associate Engineer
More informationRecognizing Handwritten Digits Using the LLE Algorithm with Back Propagation
Recognizing Handwritten Digits Using the LLE Algorithm with Back Propagation Lori Cillo, Attebury Honors Program Dr. Rajan Alex, Mentor West Texas A&M University Canyon, Texas 1 ABSTRACT. This work is
More informationData Preprocessing. Javier Béjar. URL - Spring 2018 CS - MAI 1/78 BY: $\
Data Preprocessing Javier Béjar BY: $\ URL - Spring 2018 C CS - MAI 1/78 Introduction Data representation Unstructured datasets: Examples described by a flat set of attributes: attribute-value matrix Structured
More informationClassification of Hyperspectral Data over Urban. Areas Using Directional Morphological Profiles and. Semi-supervised Feature Extraction
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL.X, NO.X, Y 1 Classification of Hyperspectral Data over Urban Areas Using Directional Morphological Profiles and Semi-supervised
More informationDetecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference Minh Dao 1, Xiang Xiang 1, Bulent Ayhan 2, Chiman Kwan 2, Trac D. Tran 1 Johns Hopkins Univeristy, 3400
More informationAarti Singh. Machine Learning / Slides Courtesy: Eric Xing, M. Hein & U.V. Luxburg
Spectral Clustering Aarti Singh Machine Learning 10-701/15-781 Apr 7, 2010 Slides Courtesy: Eric Xing, M. Hein & U.V. Luxburg 1 Data Clustering Graph Clustering Goal: Given data points X1,, Xn and similarities
More informationAlternative Statistical Methods for Bone Atlas Modelling
Alternative Statistical Methods for Bone Atlas Modelling Sharmishtaa Seshamani, Gouthami Chintalapani, Russell Taylor Department of Computer Science, Johns Hopkins University, Baltimore, MD Traditional
More informationFace Recognition Using Wavelet Based Kernel Locally Discriminating Projection
Face Recognition Using Wavelet Based Kernel Locally Discriminating Projection Venkatrama Phani Kumar S 1, KVK Kishore 2 and K Hemantha Kumar 3 Abstract Locality Preserving Projection(LPP) aims to preserve
More informationCSE 6242 A / CX 4242 DVA. March 6, Dimension Reduction. Guest Lecturer: Jaegul Choo
CSE 6242 A / CX 4242 DVA March 6, 2014 Dimension Reduction Guest Lecturer: Jaegul Choo Data is Too Big To Analyze! Limited memory size! Data may not be fitted to the memory of your machine! Slow computation!
More informationTHE detailed spectral information of hyperspectral
1358 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 8, AUGUST 2017 Locality Sensitive Discriminant Analysis for Group Sparse Representation-Based Hyperspectral Imagery Classification Haoyang
More informationFast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification
Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification July 26, 2018 AmirAbbas Davari, Hasan Can Özkan, Andreas Maier, Christian Riess Pattern Recognition
More informationPart I. Graphical exploratory data analysis. Graphical summaries of data. Graphical summaries of data
Week 3 Based in part on slides from textbook, slides of Susan Holmes Part I Graphical exploratory data analysis October 10, 2012 1 / 1 2 / 1 Graphical summaries of data Graphical summaries of data Exploratory
More informationHYPERSPECTRAL imagery has been increasingly used
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 14, NO. 5, MAY 2017 597 Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery Wei Li, Senior Member, IEEE, Guodong Wu, and Qian Du, Senior
More informationManifold Clustering. Abstract. 1. Introduction
Manifold Clustering Richard Souvenir and Robert Pless Washington University in St. Louis Department of Computer Science and Engineering Campus Box 1045, One Brookings Drive, St. Louis, MO 63130 {rms2,
More informationAssessing a Nonlinear Dimensionality Reduction-Based Approach to Biological Network Reconstruction.
Assessing a Nonlinear Dimensionality Reduction-Based Approach to Biological Network Reconstruction. Vinodh N. Rajapakse vinodh@math.umd.edu PhD Advisor: Professor Wojciech Czaja wojtek@math.umd.edu Project
More informationNonlinear Dimensionality Reduction Applied to the Classification of Images
onlinear Dimensionality Reduction Applied to the Classification of Images Student: Chae A. Clark (cclark8 [at] math.umd.edu) Advisor: Dr. Kasso A. Okoudjou (kasso [at] math.umd.edu) orbert Wiener Center
More informationPRINCIPAL components analysis (PCA) is a widely
1586 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 6, JUNE 2006 Independent Component Analysis-Based Dimensionality Reduction With Applications in Hyperspectral Image Analysis Jing Wang,
More informationEstimating basis functions for spectral sensitivity of digital cameras
(MIRU2009) 2009 7 Estimating basis functions for spectral sensitivity of digital cameras Abstract Hongxun ZHAO, Rei KAWAKAMI, Robby T.TAN, and Katsushi IKEUCHI Institute of Industrial Science, The University
More informationCIE L*a*b* color model
CIE L*a*b* color model To further strengthen the correlation between the color model and human perception, we apply the following non-linear transformation: with where (X n,y n,z n ) are the tristimulus
More informationLaplacian Faces: A Face Recognition Tool
Laplacian Faces: A Face Recognition Tool Prof. Sami M Halwani 1, Prof. M.V.Ramana Murthy 1, Prof. S.B.Thorat 1 Faculty of Computing and Information Technology, King Abdul Aziz University, Rabigh, KSA,Email-mv.rm50@gmail.com,
More informationCSE 6242 / CX October 9, Dimension Reduction. Guest Lecturer: Jaegul Choo
CSE 6242 / CX 4242 October 9, 2014 Dimension Reduction Guest Lecturer: Jaegul Choo Volume Variety Big Data Era 2 Velocity Veracity 3 Big Data are High-Dimensional Examples of High-Dimensional Data Image
More informationChap.12 Kernel methods [Book, Chap.7]
Chap.12 Kernel methods [Book, Chap.7] Neural network methods became popular in the mid to late 1980s, but by the mid to late 1990s, kernel methods have also become popular in machine learning. The first
More informationTime Series Clustering Ensemble Algorithm Based on Locality Preserving Projection
Based on Locality Preserving Projection 2 Information & Technology College, Hebei University of Economics & Business, 05006 Shijiazhuang, China E-mail: 92475577@qq.com Xiaoqing Weng Information & Technology
More informationDimension Reduction CS534
Dimension Reduction CS534 Why dimension reduction? High dimensionality large number of features E.g., documents represented by thousands of words, millions of bigrams Images represented by thousands of
More informationDimension reduction : PCA and Clustering
Dimension reduction : PCA and Clustering By Hanne Jarmer Slides by Christopher Workman Center for Biological Sequence Analysis DTU The DNA Array Analysis Pipeline Array design Probe design Question Experimental
More informationAnisotropic representations for superresolution of hyperspectral data
Anisotropic representations for superresolution of hyperspectral data Edward H. Bosch, Wojciech Czaja, James M. Murphy, and Daniel Weinberg Norbert Wiener Center Department of Mathematics University of
More information10-701/15-781, Fall 2006, Final
-7/-78, Fall 6, Final Dec, :pm-8:pm There are 9 questions in this exam ( pages including this cover sheet). If you need more room to work out your answer to a question, use the back of the page and clearly
More informationFast Anomaly Detection Algorithms For Hyperspectral Images
Vol. Issue 9, September - 05 Fast Anomaly Detection Algorithms For Hyperspectral Images J. Zhou Google, Inc. ountain View, California, USA C. Kwan Signal Processing, Inc. Rockville, aryland, USA chiman.kwan@signalpro.net
More informationsensors ISSN
Sensors 2009, 9, 196-218; doi:10.3390/s90100196 OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Article Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using
More informationLocal Linear Embedding. Katelyn Stringer ASTR 689 December 1, 2015
Local Linear Embedding Katelyn Stringer ASTR 689 December 1, 2015 Idea Behind LLE Good at making nonlinear high-dimensional data easier for computers to analyze Example: A high-dimensional surface Think
More informationTraining-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIENCE, VOL.32, NO.9, SEPTEMBER 2010 Hae Jong Seo, Student Member,
More informationGraph Matching using Spectral Embedding and Semidefinite Programming
Graph Matching using Spectral Embedding and Semidefinite Programming Xiao Bai, Hang Yu, Edwin R Hancock Computer Science Department University of York Abstract This paper describes how graph-spectral methods
More informationMulti-level fusion of graph based discriminant analysis for hyperspectral image classification
DOI 10.1007/s11042-016-4183-7 Multi-level fusion of graph based discriminant analysis for hyperspectral image classification Fubiao Feng 1 Qiong Ran 1 Wei Li 1 Received: 28 May 2016 / Revised: 28 October
More informationSchool of Computer and Communication, Lanzhou University of Technology, Gansu, Lanzhou,730050,P.R. China
Send Orders for Reprints to reprints@benthamscienceae The Open Automation and Control Systems Journal, 2015, 7, 253-258 253 Open Access An Adaptive Neighborhood Choosing of the Local Sensitive Discriminant
More informationApplications Video Surveillance (On-line or off-line)
Face Face Recognition: Dimensionality Reduction Biometrics CSE 190-a Lecture 12 CSE190a Fall 06 CSE190a Fall 06 Face Recognition Face is the most common biometric used by humans Applications range from
More informationData Preprocessing. Javier Béjar AMLT /2017 CS - MAI. (CS - MAI) Data Preprocessing AMLT / / 71 BY: $\
Data Preprocessing S - MAI AMLT - 2016/2017 (S - MAI) Data Preprocessing AMLT - 2016/2017 1 / 71 Outline 1 Introduction Data Representation 2 Data Preprocessing Outliers Missing Values Normalization Discretization
More informationPrincipal Component Image Interpretation A Logical and Statistical Approach
Principal Component Image Interpretation A Logical and Statistical Approach Md Shahid Latif M.Tech Student, Department of Remote Sensing, Birla Institute of Technology, Mesra Ranchi, Jharkhand-835215 Abstract
More informationMETHODS FOR TARGET DETECTION IN SAR IMAGES
METHODS FOR TARGET DETECTION IN SAR IMAGES Kaan Duman Supervisor: Prof. Dr. A. Enis Çetin December 18, 2009 Bilkent University Dept. of Electrical and Electronics Engineering Outline Introduction Target
More informationDiffraction gratings. e.g., CDs and DVDs
Diffraction gratings e.g., CDs and DVDs Diffraction gratings Constructive interference where: sinθ = m*λ / d (If d > λ) Single-slit diffraction 1.22 * λ / d Grating, plus order-sorting filters on detector
More informationA Shared Memory Parallel Algorithm for Data Reduction Using the Singular Value Decomposition Rhonda Phillips, Layne Watson, Randolph Wynne
A Shared Memory Parallel Algorithm for Data Reduction Using the Singular Value Decomposition Rhonda Phillips, Layne Watson, Randolph Wynne April 16, 2008 Outline Motivation Algorithms Study Area Results
More informationHyperspectral Chemical Imaging: principles and Chemometrics.
Hyperspectral Chemical Imaging: principles and Chemometrics aoife.gowen@ucd.ie University College Dublin University College Dublin 1,596 PhD students 6,17 international students 8,54 graduate students
More informationGeneralized trace ratio optimization and applications
Generalized trace ratio optimization and applications Mohammed Bellalij, Saïd Hanafi, Rita Macedo and Raca Todosijevic University of Valenciennes, France PGMO Days, 2-4 October 2013 ENSTA ParisTech PGMO
More informationBig Data Analytics. Special Topics for Computer Science CSE CSE Feb 11
Big Data Analytics Special Topics for Computer Science CSE 4095-001 CSE 5095-005 Feb 11 Fei Wang Associate Professor Department of Computer Science and Engineering fei_wang@uconn.edu Clustering II Spectral
More informationTexture Mapping using Surface Flattening via Multi-Dimensional Scaling
Texture Mapping using Surface Flattening via Multi-Dimensional Scaling Gil Zigelman Ron Kimmel Department of Computer Science, Technion, Haifa 32000, Israel and Nahum Kiryati Department of Electrical Engineering
More informationNon-linear dimension reduction
Sta306b May 23, 2011 Dimension Reduction: 1 Non-linear dimension reduction ISOMAP: Tenenbaum, de Silva & Langford (2000) Local linear embedding: Roweis & Saul (2000) Local MDS: Chen (2006) all three methods
More informationHyperspectral Image Segmentation using Homogeneous Area Limiting and Shortest Path Algorithm
Hyperspectral Image Segmentation using Homogeneous Area Limiting and Shortest Path Algorithm Fatemeh Hajiani Department of Electrical Engineering, College of Engineering, Khormuj Branch, Islamic Azad University,
More informationPoS(CENet2017)005. The Classification of Hyperspectral Images Based on Band-Grouping and Convolutional Neural Network. Speaker.
The Classification of Hyperspectral Images Based on Band-Grouping and Convolutional Neural Network 1 Xi an Hi-Tech Institute Xi an 710025, China E-mail: dr-f@21cnl.c Hongyang Gu Xi an Hi-Tech Institute
More informationUAV-based Remote Sensing Payload Comprehensive Validation System
36th CEOS Working Group on Calibration and Validation Plenary May 13-17, 2013 at Shanghai, China UAV-based Remote Sensing Payload Comprehensive Validation System Chuan-rong LI Project PI www.aoe.cas.cn
More information