Values range from 0 to 1.0.Д®NVI computes contrast using the following equation:Д®NVI computes dissimilarity using the absolute values of the greyscale differences: HomogeneityД®NVI computes homogeneity using the 'inverse difference moment' equation. This value is based on the Greyscale Quantization Level that you specify. The co-occurrence matrix in this example would be as follows:Д®NVIВ computes the following second-order co-occurrence metrics using the equations from Haralick, Shanmugan, and Dinstein (1973). Moving it one pixel to the right shifts it by 1 in the X direction and 0 in the Y direction as follows: It can move in one of the following increments:ДЄssume that you have a 3 x 3 processing window over an image whose pixel values range from 2 to 6. Haralick, Shanmugan, and Dinstein (1973) refer to this as a 'gray-tone spatial-dependence matrix.' Their implementation considers four directions (0В°, 45В°, 90В°, and 135В°) between neighboring cells that are separated by some distance, d.Д®NVI uses a similar method to compute a co-occurrence matrix by considering that each pixel (except for those on the periphery of an image) has eight neighboring pixels. It shows the number of occurrences of the relationship between a pixel and its specified neighbor. This matrix is a function of both the angular relationship and distance between two neighboring pixels. They use a co-occurrence matrix to calculate texture values. Second-order (also called co-occurrence) metrics analyze the relationship between pixel pairs. Positive values indicate data that are skewed right. Negative values indicate data that are skewed left of the mean. The skewness for a normal distribution is 0. This is a measure of symmetry around the mean. You can write a script to compute first-order entropy texture metrics using the ENVIFirstOrderEntropyTextureRaster routine. It measures the disorder of the kernel values. Entropy is calculated based on the distribution of the pixel values in the kernel. (1994) to compute entropy using the pixel values in a kernel centered at the current pixel. This is a measure of the dispersion of the values around the mean.Д®NVI uses the following equation from Anys et al. This is essentially a smoothing operation. This is the maximum value minus the minimum value in the kernel.Д®NVI computes the mean of the kernel as follows. Note how the results appear for various textures in these images. They provide examples of rough textures such as trees along with smooth, uniform textures such as building rooftops. You can compute the following first-order texture metrics:ДЄlong with the definitions of each texture measure below are images that show the results applied to WorldView-3 panchromatic scenes (40-centimeter resolution). = Number of distinct grey levels in the quantized image The equations are from Anys (1994), where: Separate images with different spatial arrangements of the same pixels within a single kernel will yield the same first-order texture value in both kernels.ДЇor each pixel in the image, ENVI obtains the normalized histogram of its kernel and computes the first-order occurrence metrics below. First-Order (Occurrence) MetricsДЇirst-order metrics operate on the counts, or occurrences, of the different digital number (DN) values within a kernel. Next are descriptions of the texture metrics that can be computed.

If the window size is too large, it could overlap different features and introduce spatial errors (Anys et al., 1994). If the window size is too small, you cannot extract enough spatial information to distinguish among different land features. The size of the kernel in part determines the success of texture-based image classification. This process continues until every pixel in the image has a texture value. The kernel moves one pixel over, and ENVIВ repeats the texture calculation.It assigns the resulting value to the center pixel in the kernel.It computes the texture metric by summing the normalized occurrence values and applying the relevant equation.It normalizes the occurrence values by dividing them by the number of values in the kernel (9 in this example).It creates a vector (1D array) of the occurrence values.ENVI computes a histogram that determines the frequency of occurrence of the quantized values within the kernel.If the DNВ values within the kernel are floating-point or double-precision, ENVI quantizes them into integer values.Once you specify the size of the kernel (for example, 3 x 3 pixels), the process for computing texture metrics begins: When combined with spectral data, texture provides another level of information for interpreting features in pixel-based classification.ДЄ moving window, or kernel, is used to compute different texture metrics such as mean, variance, and entropy. In general, texture refers to the spatial variation of image greyscale levels (tone) as a function of scale.

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