Classification and feature extraction of AVIRIS data

The processing of Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) data is discussed both in terms of feature extraction and classification. The recently proposed decision boundary feature extraction method is reviewed and then applied in experiments. Results of classifications for AVIRIS data from Iceland 1991 are given with emphasis on geological applications. The classifiers used include neural network methods and statistical approaches. The decision boundary feature extraction method shows excellent performance for these data. >

[1]  Etienne Barnard,et al.  Optimization for training neural nets , 1992, IEEE Trans. Neural Networks.

[2]  James C. Bezdek,et al.  Fuzzy models—What are they, and why? [Editorial] , 1993, IEEE Transactions on Fuzzy Systems.

[3]  James C. Bezdek,et al.  A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition , 1996, J. Intell. Fuzzy Syst..

[4]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[5]  Jon Atli Benediktsson,et al.  Consensus theoretic classification methods , 1992, IEEE Trans. Syst. Man Cybern..

[6]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[7]  David A. Landgrebe,et al.  Feature Extraction Based on Decision Boundaries , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[9]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[10]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[11]  David A. Landgrebe,et al.  Decision boundary feature extraction for nonparametric classification , 1993, IEEE Trans. Syst. Man Cybern..

[12]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[13]  Jon Atli Benediktsson,et al.  Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data , 1993 .

[14]  David A. Landgrebe,et al.  Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.