New developments and application of the UPRM MATLAB hyperspectral image analysis toolbox

The Hyperspectral Image Analysis Toolbox (HIAT) is a collection of algorithms that extend the capability of the MATLAB numerical computing environment for the processing of hyperspectral and multispectral imagery. The purpose the Toolbox is to provide a suite of information extraction algorithms to users of hyperspectral and multispectral imagery. HIAT has been developed as part of the NSF Center for Subsurface Sensing and Imaging (CenSSIS) Solutionware that seeks to develop a repository of reliable and reusable software tools that can be shared by researchers across research domains. HIAT provides easy access to feature extraction/selection, supervised and unsupervised classification algorithms, unmixing and visualization developed at Laboratory of Remote Sensing and Image Processing (LARSIP). This paper presents an overview of the tools, application available in HIAT using as example an AVIRIS image. In addition, we present the new HIAT developments, unmixing, new oversampling algorithm, true color visualization, crop tool and GUI enhancement.

[1]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[2]  Jaime José Laracuente-Díaz.  Determining noise in hyperspectral imagery for the application of oversampling to supervised classification , 2005 .

[3]  Luis O. Jimenez-Rodriguez,et al.  Unsupervised feature extraction techniques for hyperspectral data and its effects on unsupervised classification , 2003, SPIE Remote Sensing.

[4]  David A. Landgrebe,et al.  Robust parameter estimation for mixture model , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[5]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[6]  Emmanuel Arzuaga-Cruz,et al.  Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Luis O. Jimenez-Rodriguez,et al.  Unsupervised feature extraction and band subset selection techniques based on relative entropy criteria for hyperspectral data analysis , 2003, SPIE Defense + Commercial Sensing.

[8]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[9]  Miguel Velez-Reyes,et al.  An algorithm for fully constrained abundance estimation in hyperspectral unmixing , 2005 .

[10]  Chein-I Chang,et al.  Unsupervised hyperspectral image analysis with projection pursuit , 2000, IEEE Trans. Geosci. Remote. Sens..

[11]  M. Velez-Reyes,et al.  Subset selection analysis for the reduction of hyperspectral imagery , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[12]  David R. Kaeli,et al.  A MATLAB toolbox for Hyperspectral Image Analysis , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.