Paper
26 October 2011 Evaluation of textural features for multispectral images
Ulya Bayram, Gulcan Can, Sebnem Duzgun, Nese Yalabik
Author Affiliations +
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ulya Bayram, Gulcan Can, Sebnem Duzgun, and Nese Yalabik "Evaluation of textural features for multispectral images", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800I (26 October 2011); https://doi.org/10.1117/12.898292
Lens.org Logo
CITATIONS
Cited by 13 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Diffusion

Feature extraction

Remote sensing

Principal component analysis

Image filtering

Binary data

Image classification

Back to Top