This module
implements
methods to detect patterns in image regions. Commonly, classification
algorithms are divided by the level of classification (pixel or
region), and by the interaction of the user (supervised or
unsupervised). Pixel-based
algorithms classify
individual pixels
according to their resemblance to a specific pattern. Region-based
algorithms use regions from segmented images, and classify each region
to a specific pattern. Supervised
methods uses a predefined typology,
given by the user, who supplies samples of each pattern. Unsupervised
methods detect an unknown number of patterns, according to their own
method.
The available methods in TerraLib are:
It is accessible through:
Raster Processing > Classification... (list of all raster layers will be available)
This wizard consists of the following steps:
The available methods in TerraLib are:
- ISOSeg,
- Expectation-Maximization - EM,
- K-Means,
- Spectral Angle Mapper - SAM,
- Maximum a Posteriori Probability -
MAP.
ISOSeg
This is an unsupervised and region-based classification algorithm.
Input:- Raster
-
Vector of polygons
-
Acceptance threshold
Expectation-Maximization - EM
This is an unsupervised and pixel-based classification algorithm. Expectation-Maximization (EM) works iteratively by applying two steps: the E-step (Expectation) and the M-step (Maximization).
Input:
- Raster
-
The value of “K”, which stands for the number of patterns (or clusters) to find in the image.
-
The maximum number of iterations (E/M steps) to perform if convergence is not achieved.
-
The maximum number of points used to estimate the clusters (default = 1000).
-
A convergence threshold. When the clusters change in a value smaller then epsilon, the convergence is achieved.
-
The previously estimated means of the clusters (optional).
K-Means
This is an unsupervised and pixel-based classification algorithm.
Input:
- Raster
-
The value of “K”, which stands for the number of patterns to find in the image.
-
A convergence threshold. When the clusters move less than this threshold, the algorithm stops.
-
Maximum number of iterations.
Spectral Angle Mapper - SAM
This is an supervised classification algorithm.
Input:
- Raster
- A set of ROI samples.
Maximum a Posteriori Probability - MAP
This is an supervised classification algorithm.
Input:
- Raster
- A set of ROI samples.
It is accessible through:
Raster Processing > Classification... (list of all raster layers will be available)
This wizard consists of the following steps:
Wizard Page 1 - Selection of the layer to execute the operation (Layer Search)
- On the List of Layers select the raster layer to apply the operation.
- Optionally use Filter By Name field giving part of the layer name to help find the layer in the list.
- Press Next to go to next step or Cancel to close the dialog.
Wizard Page 2 - Classification parameters
- Select the type of classifier to be used.
- Select the bands to be used in the process.
- As described above, each classifier has a set of specific attributes. For supervised classifiers (SAM and MAP) is necessary to use a component for the acquisition of samples.
- Use
tool to
enable the acquisition of components (samples) through the interface Raster
Navigator. This interface offers several tools (such
as zoom, color composition) to help on collecting good samples. - Use
tool to create a region
of interest over the image that represents a homogeneous region of a
desired sample. - Press Next to go to next step, Back to return to the previous wizard or Cancel to close the dialog.
Wizard Page 3 - Output information
- Raster Info
- First press
and inform the folder where the resulting
file will be saved. - Name - inform the raster name.
- Extra Parameters - if there are some, see the details on how to inform then here.
- Press Finish to save the resulting contrasted raster or Back to go to the previous wizard page.