Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
interimage:attributes_description [2010/06/23 13:42] castejon |
interimage:attributes_description [2010/06/23 15:23] rsilva |
||
---|---|---|---|
Line 57: | Line 57: | ||
{{ :interimage:shapeindex.png }} | {{ :interimage:shapeindex.png }} | ||
Where P is the polygon perimeter and A is the area. | Where P is the polygon perimeter and A is the area. | ||
+ | |||
Line 66: | Line 67: | ||
===== Spectral Statistical Attributes ===== | ===== Spectral Statistical Attributes ===== | ||
- | * **Amplitude** - | + | * **Amplitude** - represents the difference between the maximum and minimum pixel values of a region for the given image band/channel. |
* **Brightness** - | * **Brightness** - | ||
- | * **Correlation** - Correlation is a similarity measures between two data sets under an absolute scale between [-1,1]. It is calculated as showed by the next formula: | + | * **Correlation** - Correlation is a similarity measure between two data sets under an absolute scale between [-1,1]. It is calculated as showed by the next formula: |
{{ interimage:att_correlation.gif }} | {{ interimage:att_correlation.gif }} | ||
Line 101: | Line 102: | ||
* **Variance** - Like the standart deviation, the variance also represents the numerical data dispersion degree surrounding the mean but in the original data values scale. It is defined by: | * **Variance** - Like the standart deviation, the variance also represents the numerical data dispersion degree surrounding the mean but in the original data values scale. It is defined by: | ||
{{ interimage:att_variance.gif }} | {{ interimage:att_variance.gif }} | ||
+ | |||
Line 141: | Line 143: | ||
- | * **StdDeviationGLCM** - | + | * **StdDeviationGLCM** - The standart deviation is a measure that represents the values dispersion around a GLCM mean value. The GLCM standart deviation calcule differs from the simple standart deviation because the use of co-ocurrence frequencies. The calculus is showed by the next formula where "i" and "j" are adjacent image points values under one pre-defined direction. p(i,j) is the probability of that co-ocurrence over the image. |
+ | {{ interimage:att_stddeviationglcm.gif }} | ||
===== Neighborhood Attributes ===== | ===== Neighborhood Attributes ===== |