Hyperspectral Band Selection and Endmember Detection Using Sparsity Promoting Priors

This letter presents a simultaneous band selection and endmember detection algorithm for hyperspectral imagery. This algorithm is an extension of the sparsity promoting iterated constrained endmember (SPICE) algorithm. The extension adds spectral band weights and a sparsity promoting prior to the SPICE objective function to provide integrated band selection. In addition to solving for endmembers, the number of endmembers, and end- member fractional maps, this algorithm attempts to autonomously perform band selection and to determine the number of spectral bands required for a particular scene. Results are presented on a simulated data set and the AVIRIS Indian Pines data set. Experiments on the simulated data set show the ability to find the correct endmembers and abundance values. Experiments on the Indian Pines data set show strong classification accuracies in comparison to previously published results.

[1]  Andreas T. Ernst,et al.  ICE: a statistical approach to identifying endmembers in hyperspectral images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Mingyi He,et al.  Band selection based on feature weighting for classification of hyperspectral data , 2005, IEEE Geoscience and Remote Sensing Letters.

[3]  Hao Chen,et al.  Hyperspectral feature selection for forest classification , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Qian Du,et al.  A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[5]  Paul Scheunders,et al.  A band selection technique for spectral classification , 2005, IEEE Geoscience and Remote Sensing Letters.

[6]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[7]  Joydeep Ghosh,et al.  Best-bases feature extraction algorithms for classification of hyperspectral data , 2001, IEEE Trans. Geosci. Remote. Sens..

[8]  N. Keshava,et al.  Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Wesley E. Snyder,et al.  Band selection using independent component analysis for hyperspectral image processing , 2003, 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings..

[10]  Robert I. Damper,et al.  Band Selection for Hyperspectral Image Classification Using Mutual Information , 2006, IEEE Geoscience and Remote Sensing Letters.

[11]  Chein-I Chang,et al.  Constrained band selection for hyperspectral imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Paul D. Gader,et al.  Sparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.

[13]  Ye Zhang,et al.  Unsupervised band selection method based on improved N-FINDR algorithm for spectral unmixing , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[14]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[15]  Lorenzo Bruzzone,et al.  A new search algorithm for feature selection in hyperspectral remote sensing images , 2001, IEEE Trans. Geosci. Remote. Sens..

[16]  J. B. Lee,et al.  Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .

[17]  Adolfo Martínez Usó,et al.  Automatic Band Selection in Multispectral Images Using Mutual Information-Based Clustering , 2006, CIARP.

[18]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[19]  Rick Archibald,et al.  Feature Selection and Classification of Hyperspectral Images With Support Vector Machines , 2007, IEEE Geoscience and Remote Sensing Letters.