TY - GEN
T1 - Feature extraction-selection scheme for hyperspectral image classification using fourier transform and jeffries-matusita distance
AU - Salgado, Beatriz Paulina Garcia
AU - Ponomaryov, Volodymyr
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Hyperspectral Image Classification represents a challenge because of their high number of bands, where each band represents a random variable in the classific ant or irrelevant; furthermore, it maybe not a discriminatory. Consequently, a classifier has a little biased information related to the classes resulting in lower accuracy rates. In this work, we describe a novel methodology in performing feature extraction in classification as well as in efficient feature selection based on coefficients obtained via Discrete Fourier Transform (DFT) for signals by linking the bands of the images and making a selection by Jeffries-Matusita distance criterion. To test the experimental accuracy of current proposal, we employ three hyperspectral images justifying its performance against other state-of-the-art methods using Principal Components Analysis (PCA) feature extraction algorithm in combination with the Jeffries-Matusita distance criterion for its components selection and employing a Support Vector Machine (SVM) for classification.
AB - Hyperspectral Image Classification represents a challenge because of their high number of bands, where each band represents a random variable in the classific ant or irrelevant; furthermore, it maybe not a discriminatory. Consequently, a classifier has a little biased information related to the classes resulting in lower accuracy rates. In this work, we describe a novel methodology in performing feature extraction in classification as well as in efficient feature selection based on coefficients obtained via Discrete Fourier Transform (DFT) for signals by linking the bands of the images and making a selection by Jeffries-Matusita distance criterion. To test the experimental accuracy of current proposal, we employ three hyperspectral images justifying its performance against other state-of-the-art methods using Principal Components Analysis (PCA) feature extraction algorithm in combination with the Jeffries-Matusita distance criterion for its components selection and employing a Support Vector Machine (SVM) for classification.
KW - DFT
KW - Feature extraction
KW - Hyperspectral images
KW - Jeffries-Matusita distance
KW - PCA
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84952663478&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27101-9_25
DO - 10.1007/978-3-319-27101-9_25
M3 - Contribución a la conferencia
SN - 9783319271002
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 337
EP - 348
BT - Advances in Artificial Intelligence and Its Applications - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Proceedings
A2 - Alcántara, Oscar Herrera
A2 - Lagunas, Obdulia Pichardo
A2 - Figueroa, Gustavo Arroyo
PB - Springer Verlag
T2 - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015
Y2 - 25 October 2015 through 31 October 2015
ER -