Evaluation of Textural Features for Multispectral Images

Date

2011-09-21

Author

Bayram, Ulya
Can, Gulcan
Duzgun, Sebnem
Yalabik, Nese

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Gray level co-occurrence matrix, Histogram of oriented gradients, Gabor feature, Linear binary pattern, Color histogram, Diffusion distance, Textural features, Remote sensing

URI

https://hdl.handle.net/11511/67576

DOI

https://doi.org/10.1117/12.898292

Collections

Department of Computer Engineering, Article

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Citation Formats

U. Bayram, G. Can, S. Duzgun, and N. Yalabik, “Evaluation of Textural Features for Multispectral Images,” pp. 0–0, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67576.