1. The MATLAB Hyperspectral Image Analysis Toolbox
Samuel Rosario-Torres,
Dr. Miguel Velez-Reyes,
Center for Subsurface Sensing and Imaging Systems, Tropical Center for Earth and Space Studies
University of Puerto Rico at Mayagüez, P. O. Box 9042, Mayagüez, Puerto Rico
What is the Hyperspectral Image Analysis Toolbox?
The Hyperspectral Image Analysis Toolbox (HIAT) is a collection of functions that extend the capability of the MATLAB® numeric computing environment. It has been implemented for the Macintosh and PC-Windows systems using MATLAB. It is intended for the analysis of multispectral and hyperspectral images taken from the different multispectral and hyperspectral sensors available today. The purpose of this toolbox is the development of a system that applies algorithms developed from research done in the Laboratory for Applied Remote Sensing and Image Processing (LARSIP) at the University of Puerto Rico, Mayagüez Campus.
Processing Example
Image acquired from Hyperion, a hyperspectral imager with 220 spectral bands (.4 to 2.5 µm) at 10 nm spectral resolution and a 30m spatial resolution. The area covers the area of Parguera in Lajas, Puerto Rico. This image has been collected to study the application of hyperspectral remote sensing to study the reefs and other coastal characteristics of the area. In this example, a subset of the data of 169x255 pixels and 196 bands is used.
Post-Processing Algorithms
Classification Map
MATLAB HIAT Version 1.4
Data Processing Scheme for HIAT
Gray Scale
Color Composite
True Color
ECHO Post-Classification Map
Toolbox Utilities
What Can You Do with HIAT?
Class Statistics
Pixel’s Spectral Signature
Loading Images
Matlab (*.mat)
JPEG and
Remote Sensing (*.bip, *.bil, *.bsq)
TIFF
Image Enhancement
Resolution Enhancement
PCA Filter Enhancement
Feature Extraction/Selection Algorithms
Principal Components Analysis
Singular Value Decomposition Band Subset Selection
Information Divergence Band Subset Selection
Discriminant Analysis
Information Divergence Projection Pursuit
Optimized Information Divergence Projection Pursuit
Classifiers
Euclidean Distance
Fisher’s Linear Discriminant
Angle Detection
Mahalanobis Distance
Maximum Likelihood
Abundance Estimation
Non Negative Sum To One
Non Negative Sum Less or Equal to One
Non Negative Least Square
Unconstrained
Positive Constrained
Post-Processing Algorithms
ECHO 2x2
ECHO 4x4
ECHO 3x3
Online Documentation & Help
Image Enhancement
Online Help & Documentation
Classification Process
Supervised & Unsupervised Classification
Abundance Estimation
Download the Toolbox
Go to
Click in Software link
Click in SSI Toolboxes
Click under The Hyperspectral Toolbox
Or Go To
CenSSIS VALUE ADDED
The Hyperspectral Image Analysis Toolbox will provide support for CenSSIS Academic Institutions interested in HSI. This is specifically for the R3 thrust of CenSSIS, providing for the S2 and S4 areas this type of data analysis. Its contribution extents into providing infrastructure support for the development and distribution processes and to develop guidelines and procedures for software development and testing.
Acknowledgment
Partially supported by the NSF Engineering Research Centers Program under grant ECC
Some algorithms development work was supported by:
NASA University Research Centers Program under grant NCC5-518
Department of Defense under DEPSCoR Grant DAAG
NIMA grant NMA