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.
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