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geodma_2:features [2017/03/07 17:25] raian [Landscape-based features] |
geodma_2:features [2021/08/27 19:08] tkorting [Segmentation-based spectral features] |
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| - | ====== GeoDMA 2.0 Features ====== | + | ====== GeoDMA Features ====== |
| GeoDMA has metrics integrating Polygons, Cells, and Images. Through image segmentation, GeoDMA creates Polygons. | GeoDMA has metrics integrating Polygons, Cells, and Images. Through image segmentation, GeoDMA creates Polygons. | ||
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| | Name | Description | Formula | Range | Units | | | Name | Description | Formula | Range | Units | | ||
| - | | AMPLITUDE_BAND | Defines the amplitude of the pixels inside the object. The amplitude means the maximum pixel value minus the minimum pixel value. | $amp = px_{max} - px_{min}$ | $\geq 0$ | $px$ | | + | | AMPL_BAND | Amplitude defines the amplitude of the pixels inside the object. The amplitude means the maximum pixel value minus the minimum pixel value. | $amp = px_{max} - px_{min}$ | $\geq 0$ | $px$ | |
| - | | BAND_RATIO_BAND | Describes the contribution of the given band to the region. | $bandRatio = \frac{\mu_{B_i}}{\sum_{j = 1}^{NB} \mu_{B_j}}$ | $\geq 0$ | $px$ | | + | | BRATIO_BAND | Band Ratio describes the contribution of the given band to the region. | $bandRatio = \frac{\mu_{B_i}}{\sum_{j = 1}^{NB} \mu_{B_j}}$ | $\geq 0$ | $px$ | |
| - | | COUNT_BAND | Defines the total number of pixels inside the object, including pixels with dummy values. | $N_{tot} = cout(X)$ | $\geq 0$ | $N$ | | + | | COUNT_BAND | Count defines the total number of pixels inside the object, including pixels with dummy values. | $N_{tot} = cout(X)$ | $\geq 0$ | $N$ | |
| - | | CONTRAST_SE_BAND | Returns a measure of the intensity contrast between a pixel and its southeast neighbor over the object. Contrast is 0 for a constant object. It is also known as //Sum of Squares Variance// | $Contrast = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,j} . |i - j|^2$ | $[0, (size(GLCM, 1)-1)^2]$ | - | | + | | CONTSE_BAND | Contrast SE returns a measure of the intensity contrast between a pixel and its southeast neighbor over the object. Contrast is 0 for a constant object. It is also known as //Sum of Squares Variance// | $Contrast = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,j} . |i - j|^2$ | $[0, (size(GLCM, 1)-1)^2]$ | - | |
| - | | DISSIMILARITY_SE_BAND | Measures how different the elements of the GLCM are from each other and it is high when the local region has a high contrast. | $dissimilarity = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,j}|i-j|$ | $\geq 0$ | - | | + | | DISSE_BAND | Dissimilarity SE measures how different the elements of the GLCM are from each other and it is high when the local region has a high contrast. | $dissimilarity = \sum_{i=0}^{D-1}\sum_{j=0}^{D-1}p_{i,j}|i-j|$ | $\geq 0$ | - | |
| - | | ENERGY_SE_BAND | It returns the squared root of Angular Second Moment, computed by the sum of the squared elements in GLCM. Energy is 1 for a constant image. | $energy = \sum_{i=0}^{D-1} \sum{j=0}^{D-1} p_{i,j}^2}$ | $[0,1]$ | - | | + | | ENERGSE_BAND | Energy SE returns the squared root of Angular Second Moment, computed by the sum of the squared elements in GLCM. Energy is 1 for a constant image. | $energy = \sum_{i=0}^{D-1} \sum{j=0}^{D-1} p_{i,j}^2}$ | $[0,1]$ | - | |
| - | | ENTROPY_SE_BAND | Measures the disorder in an image. When the image is not uniform, many GLCM elements have small values, resulting in large entropy. | $entropy = -\sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} p_{ij} . \log{p_{ij}}$ | $\geq 0$ | - | | + | | ENTRSE_BAND | Entropy SE measures the disorder in an image. When the image is not uniform, many GLCM elements have small values, resulting in large entropy. | $entropy = -\sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} p_{ij} . \log{p_{ij}}$ | $\geq 0$ | - | |
| - | | HOMOGENEITY_SE_BAND | Assumes higher values for smaller differences in the GLCM. Also called //Inverse Difference Moment//. Homogeneity is 1 for a diagonal GLCM. | $homog = \sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} \frac{p_{ij}}{1 + (i - j)^2}$ | $\geq 0$ | - | | + | | HOMOGSE_BAND | Homogeneity SE assumes higher values for smaller differences in the GLCM. Also called //Inverse Difference Moment//. Homogeneity is 1 for a diagonal GLCM. | $homog = \sum_{i=1}^{D - 1} \sum_{j=1}^{D - 1} \frac{p_{ij}}{1 + (i - j)^2}$ | $\geq 0$ | - | |
| - | | KURTOSIS_BAND | Returns the kurtosis value for all the valid pixels (not dummy) inside the object. | $K = \frac{n(n+1)(n-1)}{(n-2)(n-3)} . \frac{\sum_{i=1}^{N}(px_i-\mu)^4}{(\sum_{i=1}^N (px_i - \mu)^2)^2$ | $\geq 1$ | - | | + | | KURT_BAND | Kurtosis returns the kurtosis value for all the valid pixels (not dummy) inside the object. | $K = \frac{n(n+1)(n-1)}{(n-2)(n-3)} . \frac{\sum_{i=1}^{N}(px_i-\mu)^4}{(\sum_{i=1}^N (px_i - \mu)^2)^2$ | $\geq 1$ | - | |
| - | | MAXIMUM_VAL_BAND | Computes the maximum gray level value (not dummy) inside the object. | $maxVal = max(X)$ | $X$ | $px$ | | + | | MAX_VAL_BAND | Maximum Value computes the maximum gray level value (not dummy) inside the object. | $maxVal = max(X)$ | $X$ | $px$ | |
| - | | MEAN_BAND | Computes the average value for all $N$ pixels inside the object. | $\mu = \frac{\sum_{i=1}^N px_i}{N}$ | $\geq 0$ | $px$ | | + | | MEAN_BAND | Mean computes the average value for all $N$ pixels inside the object. | $\mu = \frac{\sum_{i=1}^N px_i}{N}$ | $\geq 0$ | $px$ | |
| - | | MEDIAN_BAND | Computes the median for all $N$ pixels inside the object. | $median = p_{\frac{(n+1)}{2}}$ | $X$ | $px$ | | + | | MEDIAN_BAND | Median computes the median for all $N$ pixels inside the object. | $median = p_{\frac{(n+1)}{2}}$ | $X$ | $px$ | |
| - | | MINIMUM_VAL_BAND | Computes the minimum gray level value (not dummy) inside the object. | $minVal = min(X)$ | $X$ | $px$ | | + | | MIN_VAL_BAND | Minimum Value computes the minimum gray level value (not dummy) inside the object. | $minVal = min(X)$ | $X$ | $px$ | |
| - | | MODE_BAND | Returns the most occurring gray level value (mode) for all $N$ (not dummy) pixels inside the object. When the object is multimodal, the first value is assumed. | | $X$ | $px$ | | + | | MODE_BAND | Mode returns the most occurring gray level value (mode) for all $N$ (not dummy) pixels inside the object. When the object is multimodal, the first value is assumed. | | $X$ | $px$ | |
| - | | NUM_MODES_BAND | Returns the number of modes for the object. | | $\geq 1$ | $px$ | | + | | N_MOD_BAND | Num Modes returns the number of modes for the object. | | $\geq 1$ | $px$ | |
| - | | SKEWNESS_BAND | Returns the skewness value for all the valid pixels (not dummy) inside the object. | $S = \frac{n\sqrt{n-1}}{n-2} . \frac{\sum_{i=1}^{N}(px_i-\mu)^3}{(\sum_{i=1}^N (px_i - \mu)^2)^\frac{3}{2}$ | - | - | | + | | SKEW_BAND | Returns the Skewness value for all the valid pixels (not dummy) inside the object. | $S = \frac{n\sqrt{n-1}}{n-2} . \frac{\sum_{i=1}^{N}(px_i-\mu)^3}{(\sum_{i=1}^N (px_i - \mu)^2)^\frac{3}{2}$ | - | - | |
| - | | STD_DEVIATION_BAND | Returns the standard deviation of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $\sigma = \sqrt{\frac{1}{N-1}\sum_{i=1}^N \left(px_i - \mu \right)^2}$ | $\geq 0$ | $px$ | | + | | STDDEV_BAND | Returns the Standard Deviation of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $\sigma = \sqrt{\frac{1}{N-1}\sum_{i=1}^N \left(px_i - \mu \right)^2}$ | $\geq 0$ | $px$ | |
| - | | SUM_BAND | Returns the sum of all $N$ (not dummy) pixels inside the object. | $sum = \sum_{i = 1}^{N}px_i$ | $\geq 0$ | $px$ | | + | | SUM_BAND | Returns the Sum of all $N$ (not dummy) pixels inside the object. | $sum = \sum_{i = 1}^{N}px_i$ | $\geq 0$ | $px$ | |
| - | | VALID_COUNT_BAND | Defines the total number of pixels inside the object with not dummy values. | $N = count(X)$ | $\geq 0$ | $N$ | | + | | VLDCNT_BAND | Valid Count defines the total number of pixels inside the object with not dummy values. | $N = count(X)$ | $\geq 0$ | $N$ | |
| - | | VAR_COEFF_BAND | Returns the coefficient of variation of the values for all the valid pixels (not dummy) inside the object | $c_v = \frac{100\sigma}{\mu}$ | $[0,100]$ | % | | + | | VARCOEF_BAND | Returns the Coefficient of Variation of the values for all the valid pixels (not dummy) inside the object | $c_v = \frac{100\sigma}{\mu}$ | $[0,100]$ | % | |
| - | | VARIANCE_BAND | Returns the variance of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $s^2 = \frac{\sum_{i=1}^N \left(px_i - \mu \right)^2}{N-1}$ | $\geq 0$ | $px$ | | + | | VAR_BAND | Returns the Variance of all $N$ (not dummy) pixels ($\mu$ is the mean value) inside the object. | $s^2 = \frac{\sum_{i=1}^N \left(px_i - \mu \right)^2}{N-1}$ | $\geq 0$ | $px$ | |
| ===== Segmentation-based spatial features ===== | ===== Segmentation-based spatial features ===== | ||
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| | POL_FRACTALDIM | Returns the fractal dimension of an object. | $2 \frac{\log {\frac{\textit{perimeter}}{4}} }{\log area}$ | $[1, 2]$ | - | | | POL_FRACTALDIM | Returns the fractal dimension of an object. | $2 \frac{\log {\frac{\textit{perimeter}}{4}} }{\log area}$ | $[1, 2]$ | - | | ||
| | POL_GYRATION_RATIUS | This feature is equals the average distance between each vertex of the polygon and it's centroid. The more similar to a circle is the object, the more likely the centroid will be inside it, and therefore this feature will be closer to 0. | $\frac{\sum{\sqrt{(x_i - x_C)^2 + (x_i - x_C)^2}}}{N}$ | $\geq 0$ | $[m, degrees, ...]$ | | | POL_GYRATION_RATIUS | This feature is equals the average distance between each vertex of the polygon and it's centroid. The more similar to a circle is the object, the more likely the centroid will be inside it, and therefore this feature will be closer to 0. | $\frac{\sum{\sqrt{(x_i - x_C)^2 + (x_i - x_C)^2}}}{N}$ | $\geq 0$ | $[m, degrees, ...]$ | | ||
| + | | POL_RADIUS | Returns the polygon radius. It corresponds to the maximum distance between the polygon centroid its vertexes. | $R$ | $>0$ | - | | ||
| | POL_BBOX_LENGTH | It is the height of the object's bounding box, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m, degrees, ...]$ | | | POL_BBOX_LENGTH | It is the height of the object's bounding box, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m, degrees, ...]$ | | ||
| | POL_PERIMETER | Returns the perimeter of the object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,degrees,...]$ | | | POL_PERIMETER | Returns the perimeter of the object, measured in the unit of measure of the current Spatial Reference System. | | $\geq 0$ | $[m,degrees,...]$ | | ||
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| | c_IJI | IJI stands for Interspersion and Juxtaposition Index. The observed interspersion over the maximum possible interspersion for the given number of patch types. It only exists for $n > 3$. | $IJI = \frac{-\sum_{j=1}^n (\frac{e_j}{\sum_{k=1}^n e_k}) \times \ln{(\frac{e_j}{\sum_{k=1}^n e_k})}}{\ln(m - 1)} \times 100$ | $[0, 100]$ | $\%$ | | | c_IJI | IJI stands for Interspersion and Juxtaposition Index. The observed interspersion over the maximum possible interspersion for the given number of patch types. It only exists for $n > 3$. | $IJI = \frac{-\sum_{j=1}^n (\frac{e_j}{\sum_{k=1}^n e_k}) \times \ln{(\frac{e_j}{\sum_{k=1}^n e_k})}}{\ln(m - 1)} \times 100$ | $[0, 100]$ | $\%$ | | ||
| | c_TABO | TABO stands for the Total Area of the Biggest Object that intersects the landscape. | | | $ha$ | | | c_TABO | TABO stands for the Total Area of the Biggest Object that intersects the landscape. | | | $ha$ | | ||
| + | | c_BIA | BIA stands for the Biggest Intersection Area. | | | $ha$ | | ||
| + | | c_TAOBIA | TAOBIA stands for the Total Area of the Object with Biggest Intersection Area. | | | $ha$ | | ||
| | PR | PR stands for Patch Richness, which is equals the number of different patch types (classes) present within the landscape boundary. | $PR = m$ | $\geq0$ | | | | PR | PR stands for Patch Richness, which is equals the number of different patch types (classes) present within the landscape boundary. | $PR = m$ | $\geq0$ | | | ||
| | PRD | PRD stands for Patch Richness Density, which is equals the number of different patch types present within the landscape boundary divided by total landscape area ($m^2$), multiplied by $10,000$ and $100$ (to convert to $100$ hectares). Note, total landscape area ($A$) includes any internal background present. | $PRD = \frac{m}{A} \times 10000 \times 100$ | $\geq0$ | $Number/100 ha$ | | | PRD | PRD stands for Patch Richness Density, which is equals the number of different patch types present within the landscape boundary divided by total landscape area ($m^2$), multiplied by $10,000$ and $100$ (to convert to $100$ hectares). Note, total landscape area ($A$) includes any internal background present. | $PRD = \frac{m}{A} \times 10000 \times 100$ | $\geq0$ | $Number/100 ha$ | | ||
