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oacalcatt(fili, dbic, chnalias, filv, dbvs, filo, dbov, ftype, statatt, texatt, texwinsz, geoatt, ppixatt, index, visirchn)
Name | Type | Caption | Length | Value range |
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FILI* | str | Input file name | 1 - | |
DBIC | List[int] | Input channels | 0 - | |
CHNALIAS | str | Aliases of input channels | 0 - | |
FILV | str | Name of input vector file | 0 - | |
DBVS* | List[int] | Segment number of vector layer | 1 - 1 | |
FILO | str | Output vector file name | 0 - | |
DBOV | List[int] | Output vector segment | 0 - 1 | |
FTYPE | str | Output file type | 0 - 3 | PIX | SHP Default: PIX |
STATATT | str | Statistical attributes to calculate | 0 - | |
TEXATT | str | Texture attributes to calculate | 0 - | |
TEXWINSZ | List[int] | Size of texture window | 0 - 1 | Default: 11 |
GEOATT | str | Geometrical attributes to calculate | 0 - | |
PPIXATT | str | Calculate pure pixels | 0 - 3 | NO | YES Default: NO |
INDEX | str | Vegetation Indices | 0 - | |
VISIRCHN | List[int] | Input channel or channels | 0 - 4 | - |
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FILI
The name of the PCIDSK file that contains the channels to process.
DBIC
The channel or channels from which to extract attributes.
By default, all channels are processed.
CHNALIAS
Comma-separated aliases, or brief descriptions, of each channel specified as input.
The field names will appear in the attribute table with the aliases you specify.
If you do not specify a value, an alias is generated based on GDB-provided channel metadata, if it exists. If the channel metadata is unavailable, a generic sequence of bands is used; for example, B01, B02, B01, and so on.
FILV
The name of the file that contains a vector layer of closed polygons; typically, a segmentation layer generated by OASEG.
If you do not specify a value, the PCIDSK file you specified as input is used.
DBVS
The vector layer to which to write the calculated attributes.
FILO
The file containing the segmentation layer to which to write the calculated attributes.
The file name you specify can be that of the input file or a new output file. When you create a new output file, the objects and attributes you specified are also written to the file.
If you do not specify a value, the segmentation layer is written to the corresponding layer of the input file.
DBOV
The segment number of the vector layer of the output file to which to write the results of the attribute calculation.
When FILO does not exist, DBOV will be ignored. The results will be written to the new file.
FTYPE
The format of the output file.
The default is PIX.
STATATT
The statistical attributes to calculate for each object of the input segmentation vector layer from the input channels.
You can also specify a subset of any statistical attributes. For example, to specify minimum and mean, enter "MIN, MEAN".
TEXATT
The texture attributes to calculate for each object of the input segmentation vector layer from the input raster layers.
The attributes are based on second-order statistics calculated from the gray-level cooccurrence matrices.
You can also specify a subset of any textural attributes. For example, to specify cooccurrence standard deviation and cooccurrence contrast, enter "TSTD, TCON".
TEXWINSZ
The size of the window to use to extract texture attributes.
The value you specify must be an odd integer and a minimum of 5.
The default size is 11.
GEOATT
The geometrical attributes to calculate for each object of the input segmentation vector layer.
By using a comma-separated string, you can define a subset of any geometrical attributes. For example, to specify form factor, solidity, and minor-axis length, enter "FOR, SOL, MIA".
PPIXATT
Calculate pure pixels.
This parameter is optional.
INDEX
The Vegetation Indices (VI) to calculate.
You can enter a single index or enter a comma-separated string of one or more.
To calculate all VI relevant to the input data, enter ALL.
This parameter is optional.
VISIRCHN
The input channel or channels that contain the blue, green, red, and near infrared (NIR) bands.
If you do not specify a value, the channel-level metadata is used to calculate the indices. If channel-level metadata is unavailable, or if there is more than one appropriate spectral band, you can specify a string of channels to use. The channels in the string must be in a specific order; that is, the first channel must correspond to the blue band, the second to the green band, the third to the red band, and the fourth to the NIR band.
For example, to specify that channel 2 (%2) is the blue band, channel 3 (%3) is the green band, channel 4 (%4) is the red band, and channel 8 (%8) is the NIR band, enter "2,3,4,8".
If you do not specify a value, the channel-level metadata is used to determine the relevant spectral bands.
This parameter is optional.
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Supported channels
Statistical attributes
Statistical attributes are calculated based on the image pixels inside an object. Attributes are calculated for each of the selected image bands and added to the attribute table of the vector segment layer as new fields (attributes). The following table provides a description of statistical attributes you can calculate.
Attribute | Short name | Description |
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Min. | Min | Minimum value of the pixels beneath an object in a selected band |
Max. | Max | Maximum value of the pixels beneath an object in a selected band |
Mean | Mean | Mean value of the pixels beneath an object in a selected band |
Standard deviation | StdV | Standard-deviation value of the pixels beneath an object in a selected band |
Imagery and data sets of various resolutions are often used in an object-based image analysis (OBIA) to combine information from (a) high-spatial-resolution panchromatic or wide-band multispectral imagery acquired at a low-temporal resolution with (b) imagery from narrow-band multispectral high-temporal resolution with a low-spatial resolution. This fusion of information of various resolutions is used commonly with multitemporal image analysis.
Pixels along the borders of segments contain information from the adjoining segments and act as mixed pixels. This can have substantial impact on the local mean of a segment. To overcome the mixed-pixels issue, you can opt to calculate statistics by excluding the pixels on the segment boundary. This is referred to as pure-pixel statistics.
Geometrical attributes
The attributes representing geometrical characteristics of an object (polygon segment) make object-based image analysis (OBIA) advantageous over pixel-based measures. The geometrical attributes are calculated by analyzing the polygon boundary created during segmentation, so raster information is not required. Many of the shape descriptors used commonly are calculated. Each is described in the following table.
Attribute | Short name | Description |
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Compactness | Compact | A measure of closely packed a shape is. A circle is the most compact.
Compactness = √(4 × As ÷ pi) ÷ O Where As is the area of the object, and O is the perimeter of the object (outer contour). |
Elongation | Elongation | The ratio of the height and width of a rotated, minimal-bounding box; that is, rotate a rectangle so it is the smallest rectangle in which the object fits, and then compare its height with its width.
Elongation = major axis length ÷ minor axis length |
Circularity | Circular | A ratio representing how like is the shape of the object to a circle. Circularity is the ratio of the area of a shape to the perimeter of the square of the shape.
Circularity = As ÷ O2 Where As is the area of the shape, and O2 is square of the perimeter of the shape. |
Rectangularity | Rectangular | How rectangular a shape is; that is, how much it fills its minimum bounding rectangle:
Rectangularity = As ÷ AR Where As is the area of a shape, and AR is the area of the minimum bounding rectangle. |
Convexity | Convexity | The relative amount that an object differs from a convex object. Convexity is calculated by forming the ratio of the perimeter of the convex hull of an object to the perimeter of the object itself.
Convexity = convex-hull perimeter ÷ object perimeter |
Solidity | Solidity | The density of an object. Solidity is calculated as the ratio of the area of an object to the area of a convex hull of the object. A value of 1 indicates a solid object, and a value less than 1 indicates an object having an irregular boundary or containing holes.
Solidity = area ÷ convex-hull area |
Form factor | FormFactor | The measure that compares the area of a polygon to the square of the perimeter. The form-factor value of a circle is 1, and the value of a square is pi ÷ 4.
Form factor = 4 × pi × area ÷ sqrt (perimeter) |
Major-axis length | MajorAxis | The length of the major axis of an oriented bounding box enclosing the polygon.
Values are map units of the pixel size. If the image is not georeferenced, the values are pixels. |
Minor-axis length | MinorAxis | The length of the minor axis of an oriented bounding box enclosing the polygon.
Values are map units of the pixel size. If the image is not georeferenced, the values are pixels. |
Vegetation Indices attributes
You can select from several Vegetation Indices (VI) to perform quantitative and qualitative evaluations of vegetation cover, vigor, growth dynamics, and more.
If the bands are not defined in the metadata, you can define them.
Vegetation Indices (VI) attributes are independent from the channel aliases and instead rely on the channel metadata or the channels specified as input.
The following table provides descriptions of each Vegetation Index attribute that you can calculate.
Attribute | Short name | Description |
---|---|---|
Green/Red Vegetation Index | GRVI | GRVI = (Green - Red) ÷ (Green + Red) |
Greenness Index | GI | GI = ((2.0 × Green) - (Red + Blue)) ÷ ((2.0 × Green) + Red + Blue) |
Vegetation Difference Index | VDI | VDI = NIR - Red |
Ratio Vegetation Index | RVI | RVI = NIR ÷ Red |
Normalized Difference Vegetation | NDVI | NDVI = (NIR - Red) ÷ (NIR + Red) |
Transformed Difference Vegetation Index | TDVI | TDVI = (NIR - Red) ÷ (NIR + Red) + .5 |
Soil Adjusted Vegetation Index | SAVI | SAVI = ((NIR - Red) ÷ (NIR + Red + L)) × (1 + L)
The L-value is based on the amount of green vegetative cover. L is a default of 0.5, which means, generally, areas of moderate green vegetative cover. |
Modified Soil Adjusted Vegetation Index | MSAVI2 | MSAVI2 = (0.5) × (2(NIR + 1) - √((2 × NIR + 1) - 8(NIR - Red))) |
Global Environmental Monitoring Index | GEMI | GEMI = eta × (1 - 0.25 × eta) - ((Red - 0.125) ÷ (1 - Red))
Where eta = (2 × (NIR - Red) + 1.5 × NIR + 0.5 × Red) ÷ (NIR + Red + 0.5) |
Leaf Area Index | LAI | LAI = (3.618 × EVI) - 0.118
Where EVI (Enhanced Vegetation Index) = 2.5 × (NIR - Red) ÷ (1 + NIR + (6 × Red) - (7.5 × Blue) |
Texture attributes
You can calculate texture measures for a specific direction or directional invariant for all pixels in an input image. The measures are based on second-order statistics calculated from the gray-level cooccurrence matrices.
Attribute | Short name | Description | Algorithm | Python | EASI | Focus/Modeler |
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Mean | Tex_Mean_<ch_alias_name> | Cooccurrence mean | TEX |
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Standard deviation | Tex_Std_<ch_alias_name> | Cooccurrence standard deviation | TEX |
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Entropy | Tex_Ent_<ch_alias_name> | Cooccurrence entropy | TEX |
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Angular second moment | Tex_Ang2m_<ch_alias_name> | Cooccurrence angular second moment | TEX |
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Contrast | Tex_Cont_<ch_alias_name> | Cooccurrence contrast | TEX |
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Band aliases
Aliases supplement the organization of the attributes you calculate. With attributes calculated from image layers, corresponding fields are created in the attribute table of segments. To distinguish among the various attributes, the field name is appended with the relevant layer identifier. By using aliases, you can better manage and interpret the information in the attribute table, which you can view in Attribute Manager.
During attribute calculation, the metadata is analyzed for identification of various band names and short names based on the sensor and wavelength range.
Aliases are generated based on the GDB-provided channel-metadata tag, if it is present. With optical data, aliases are generated from the BandDescription tag and, with SAR data, from the Polorization tag. If the metadata is found, a generic short name, such as B01, B02, B03, and so on is applied as an alias. You can, however, modify the aliases to suit.
The following table shows an example of the channel-level metadata tags and aliases for a sample optical image, LC08_L1TP_018026_20160925_20170221.pix.
Input channel | LC08_L1TP_018026_20160925_20170221 | Channel-metadata tag BandDescription | Alias |
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1 | Landsat-8 Coastal Aerosol (0.435 - 0.451 um) | Coastal Aerosol | Aero |
2 | Landsat-8 Blue (0.452 - 0.512 um) | Blue | Bl |
3 | Landsat-8 Green (0.533 - 0.590 um) | Green | Gr |
4 | Landsat-8 Red (0.636 - 0.673 um) | Red | Red |
5 | Landsat-8 Near Infrared (0.851 - 0.879 um) | Near Infrared | NIR |
6 | Landsat-8 SWIR 1 (1.566 - 1.651 um) | SWIR 1 | S1 |
7 | Landsat-8 SWIR 2 (2.107 - 2.294 um) | SWIR 2 | S2 |
8 | Landsat-8 Cirrus (1.363 - 1.384 um) | Cirrus | Cir |
DBIC=3,4,5 CHNALIAS="Gr, Red, NIR" STATTATT="Mean, Min"
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from pci.oacalcatt import oacalcatt fili= "l7_ms.pix" dbic=[2,3,4,5] # Calculate only the attributes for channels 2, 3, 4 and 5. chnalias="green, red, nir, swir1" # Band aliases filv="l7_ms_seg25_0.5_0.5.pix" dbvs=[2] filo="" # Blank, calculated attributes will be to filv/dbvs dbov=[2] ftype="PIX" statatt="all" # Calculate all statistical attributes texatt="tmean, tstd" # Calculate only the mean and standard deviation texture attributes texwinsz=[11] geoatt="" # None - Do not calculate geometrical attributes ppixatt="No" index="ndvi, lai, gemi, vdi" visirchn=[] oacalcatt ( fili, dbic, chnalias, filv, dbvs, filo, dbov, ftype, statatt, texatt, texwinsz, geoatt, ppixatt, index, visirchn)
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Jinru Xue and Baofeng Su, "Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications," Journal of Sensors vol. 2017, Article ID 1353691, 17 pages, 2017.
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