Share Email Print
cover

Proceedings Paper

Evaluation of textural features for multispectral images
Author(s): Ulya Bayram; Gulcan Can; Sebnem Duzgun; Nese Yalabik
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Remote sensing is a field that has wide use, leading to the fact that it has a great importance. Therefore performance of selected features plays a great role. In order to gain some perspective on useful textural features, we have brought together state-of-art textural features in recent literature, yet to be applied in remote sensing field, as well as presenting a comparison with traditional ones. Therefore we selected most commonly used textural features in remote sensing that are grey-level co-occurrence matrix (GLCM) and Gabor features. Other selected features are local binary patterns (LBP), edge orientation features extracted after applying steerable filter, and histogram of oriented gradients (HOG) features. Color histogram feature is also used and compared. Since most of these features are histogram-based, we have compared performance of bin-by-bin comparison with a histogram comparison method named as diffusion distance method. During obtaining performance of each feature, k-nearest neighbor classification method (k-NN) is applied.

Paper Details

Date Published: 27 October 2011
PDF: 14 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800I (27 October 2011); doi: 10.1117/12.898292
Show Author Affiliations
Ulya Bayram, ODTU Teknokent (Turkey)
Gulcan Can, Middle East Technical Univ. (Turkey)
Sebnem Duzgun, Middle East Technical Univ. (Turkey)
Nese Yalabik, ODTU Teknokent (Turkey)


Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top