TY - GEN
T1 - Feature extraction scheme for a textural hyperspectral image classification using gray-scaled HSV and NDVI image features vectors fusion
AU - Garcia-Salgado, B. P.
AU - Ponomaryov, V.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/3/21
Y1 - 2016/3/21
N2 - Hyperspectral images can be represented as a cube data structure. As a consequence, a spatial classification could be a difficult task. In this work, we describe a novel feature extraction methodology in order to perform a Hyperspectral image spatial classification. We turn the hyperspectral data into a gray-scaled HSV image and a Normalize Difference Vegetation Index (NDVI) representation. Afterwards, Haralick texture features are computed for both images, and the resulted features vectors are fused calculating the determinants of the matrices composed of these characteristics. To test the experimental accuracy of the proposed method, we employ five Hyperspectral images and a Maximum Likelihood Classifier (MLC). The current proposal is compared against other state-of-the-art methods, such as the employment of Principal Components Analysis (PCA).
AB - Hyperspectral images can be represented as a cube data structure. As a consequence, a spatial classification could be a difficult task. In this work, we describe a novel feature extraction methodology in order to perform a Hyperspectral image spatial classification. We turn the hyperspectral data into a gray-scaled HSV image and a Normalize Difference Vegetation Index (NDVI) representation. Afterwards, Haralick texture features are computed for both images, and the resulted features vectors are fused calculating the determinants of the matrices composed of these characteristics. To test the experimental accuracy of the proposed method, we employ five Hyperspectral images and a Maximum Likelihood Classifier (MLC). The current proposal is compared against other state-of-the-art methods, such as the employment of Principal Components Analysis (PCA).
KW - Determinants
KW - Feature Extraction
KW - Features Fusion
KW - HSV Color Space
KW - Hyperspectral Images
KW - NDVI Images
UR - http://www.scopus.com/inward/record.url?scp=84966658549&partnerID=8YFLogxK
U2 - 10.1109/CONIELECOMP.2016.7438573
DO - 10.1109/CONIELECOMP.2016.7438573
M3 - Contribución a la conferencia
AN - SCOPUS:84966658549
T3 - 2016 International Conference on Electronics, Communications and Computers, CONIELECOMP 2016
SP - 186
EP - 191
BT - 2016 International Conference on Electronics, Communications and Computers, CONIELECOMP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Electronics, Communications and Computers, CONIELECOMP 2016
Y2 - 24 February 2016 through 26 February 2016
ER -