ILMSImage Attribute Calculation
From ILMS-Wiki
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! width="5%" | Attribute type !! width="12%" | Attribute !! width="12%" | GUI-Label !! width="16%" | Internal designation !! width="55%" | Description | ! width="5%" | Attribute type !! width="12%" | Attribute !! width="12%" | GUI-Label !! width="16%" | Internal designation !! width="55%" | Description | ||
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| − | | Basis || average value per band || ''average value per input band'' || <code><layer_id>[_< | + | | Basis || average value per band || ''average value per input band'' || <code><layer_id>[_<numerator></code>] || For every input band an average value is calculated from all image pixels summarized in one cell. The internal designation corresponds to the identifier which is selected for the corresponding band (in the example: <code>panchromatic</code>), in case of mult-channel input data completed by one channel number (in the example: <code>multi-spectral_1</code>, <code>multi-spectral_2</code>, etc.). |
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| rowspan="4" | structure || linear texture || ''linear texture'' || <code>linear</code> || The activation of this measure creates the 1st degree texture in a 3x3 kernel. The algorithm calculates the texture for every cell individually and ignores all image pixels which are outside of the current cell. Like all structural features also the linear texture uses the first channel which is displayed in the project information as a basis for calculation. | | rowspan="4" | structure || linear texture || ''linear texture'' || <code>linear</code> || The activation of this measure creates the 1st degree texture in a 3x3 kernel. The algorithm calculates the texture for every cell individually and ignores all image pixels which are outside of the current cell. Like all structural features also the linear texture uses the first channel which is displayed in the project information as a basis for calculation. | ||
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| normalized texture || ''Normalized texture'' || <code>normalized</code> || The normalized textures uses the same algorithm as the linear texture but it normalizes the result additionally with the average value of all image pixels of the current cell. Like all structural features also the normalized texture uses the first channel which is displayed in the project information as a basis for calculation. | | normalized texture || ''Normalized texture'' || <code>normalized</code> || The normalized textures uses the same algorithm as the linear texture but it normalizes the result additionally with the average value of all image pixels of the current cell. Like all structural features also the normalized texture uses the first channel which is displayed in the project information as a basis for calculation. | ||
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| − | | Inverse texture || '' | + | | Inverse texture || ''inverse texture'' || <code>inverse</code> || By activating the inverse texture the ''Inverse Difference Moment'' ([http://ieeexplore.ieee.org/xpls/abs_all.jsp?tp=&isnumber=4309300&arnumber=4309314&tag=1 Haralick et al. (1973), S. 619]) is calculated on pixel level. Inverse texture uses the same algorithm as the linear and normalized texture but it finally calculates the inverse value of the square differences. Like all structural features also the inverse texture uses the first channel which is displayed in the project information as a basis for calculation. |
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| − | | Laplace texture || ''Laplace texture'' || <code>laplace</code> || This structural feature uses the | + | | Laplace texture || ''Laplace texture'' || <code>laplace</code> || This structural feature uses the modified Laplace kernel for contrast enhancement for individual cells. The parameter enhances the result with a generic kernel which reacts to local contrast maxima with minimal spatial distance (two to five pixels). As a result, isolated lines are reduced and regions are filled with spatially dense contrasts. |
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| − | | rowspan="2" | | + | | rowspan="2" | statistics || standard deviation || ''standard deviation'' || <code>stddev</code> || By activating this option the standard deviation of all image pixels summarized in one cell of the first channel in the project information is calculated. |
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| − | | | + | | cariance || ''variance'' || <code>variance</code> || By activating this option the variance of all image pixels summarized in one cell of the first channel in the project information is calculated. |
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| − | | rowspan="4" | | + | | rowspan="4" | cell geometry || cell size || rowspan="4" | without GUI link|| <code>size</code> || The size of one cell in image pixels of the most highly resolved input data channel. |
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| − | | | + | | cell perimeter|| <code>perimeter</code> || The perimeter of one cell in image pixels of the most highly resolved input data channel. |
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| − | | | + | | cell length|| <code>length</code> || The length of a cell in image pixels of the most highly resolved input data channel. |
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| − | | | + | | cell width|| <code>width</code> || The width of a cell in image pixels of the most highly resolved input data channel. |
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|} | |} | ||
| − | + | After the calculation of the selected attributes they are summarized in a table. | |
===Adding Additional Data Layers=== | ===Adding Additional Data Layers=== | ||
Revision as of 15:47, 25 July 2011
Contents |
50px| ILMSImage Attribute Calculation
Introduction
ILMSImage Attribute Calculation is part of the ILMSImage plug-in for QuantumGIS and is used for the derivation and calculation of cell-based attributes and for the integration of additional data layers.
Similar to various ILMSImage panels it consisits of two components, ILMSImage Project Information in the upper section and the actual tools in the lower section.
Das ILMSImage-Panel zur Berechnung von Zellattributen
Attribute Calculation
Background
...
Overview
The panel allows the following functions for the user:
- Calculation of default cell-based attributes: In addition to features of cell geometry basic attributes, different structural features and statistical measures belong to the implemented parameters. After the calculation those are summarized in form of an attribute table.
- Adding additional data layers: The corresponding data layers are selected by the user, an identifier is assigned and added to the existing data layers. If necessary, the data type is automatically adapted and the selected additional data are cut in terms of space.
- Visualization of selected attributes: For this purpose the attributes which are summarized in a database table are transformed into raster data and prepared in QuantumGIS for visualization.
Calculation of Cell-Based Attributes
In order to derive specific attributes for the samples from the link of cell geometry and selected input data, the relevant parameters have to be activated by clicking on the corresponding check box. The default attributes are basic values, structural features, statistical parameters and the implicitly calculated features of cell geometry.
| Attribute type | Attribute | GUI-Label | Internal designation | Description |
|---|---|---|---|---|
| Basis | average value per band | average value per input band | <layer_id>[_<numerator>] |
For every input band an average value is calculated from all image pixels summarized in one cell. The internal designation corresponds to the identifier which is selected for the corresponding band (in the example: panchromatic), in case of mult-channel input data completed by one channel number (in the example: multi-spectral_1, multi-spectral_2, etc.).
|
| structure | linear texture | linear texture | linear |
The activation of this measure creates the 1st degree texture in a 3x3 kernel. The algorithm calculates the texture for every cell individually and ignores all image pixels which are outside of the current cell. Like all structural features also the linear texture uses the first channel which is displayed in the project information as a basis for calculation. |
| normalized texture | Normalized texture | normalized |
The normalized textures uses the same algorithm as the linear texture but it normalizes the result additionally with the average value of all image pixels of the current cell. Like all structural features also the normalized texture uses the first channel which is displayed in the project information as a basis for calculation. | |
| Inverse texture | inverse texture | inverse |
By activating the inverse texture the Inverse Difference Moment (Haralick et al. (1973), S. 619) is calculated on pixel level. Inverse texture uses the same algorithm as the linear and normalized texture but it finally calculates the inverse value of the square differences. Like all structural features also the inverse texture uses the first channel which is displayed in the project information as a basis for calculation. | |
| Laplace texture | Laplace texture | laplace |
This structural feature uses the modified Laplace kernel for contrast enhancement for individual cells. The parameter enhances the result with a generic kernel which reacts to local contrast maxima with minimal spatial distance (two to five pixels). As a result, isolated lines are reduced and regions are filled with spatially dense contrasts. | |
| statistics | standard deviation | standard deviation | stddev |
By activating this option the standard deviation of all image pixels summarized in one cell of the first channel in the project information is calculated. |
| cariance | variance | variance |
By activating this option the variance of all image pixels summarized in one cell of the first channel in the project information is calculated. | |
| cell geometry | cell size | without GUI link | size |
The size of one cell in image pixels of the most highly resolved input data channel. |
| cell perimeter | perimeter |
The perimeter of one cell in image pixels of the most highly resolved input data channel. | ||
| cell length | length |
The length of a cell in image pixels of the most highly resolved input data channel. | ||
| cell width | width |
The width of a cell in image pixels of the most highly resolved input data channel. |
After the calculation of the selected attributes they are summarized in a table.
