| Environments | PYTHON :: EASI :: MODELER |
| Batch Mode | Yes |
| Quick links | Description :: Parameters :: Parameter descriptions :: Details :: Related |
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| Name | Type | Length | Value range |
|---|---|---|---|
| Reporting Units | String | 0 - 1 | |
| Input: Input theme map channel * | Raster port | 1 - 1 | |
| InputSubArea: Input sub-areas channel | Raster port | 0 - 1 | |
| InputSIG: Class signature subset 1 | SIG port | 0 - 16 | -256 - |
| Show Confusion Matrix | String | 0 - 1 | YES | NO Default: NO |
| Mask: Area mask | Bitmap port | 0 - 4 | |
| Report | String | 0 - 192 | See parameter description |
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Reporting Units
Specifies the units of measurement in which the results will be reported.
Input: Input theme map channel
Specifies the input channel that contains the theme map.
This is typically an output channel from MLC which contains classified data.
InputSubArea: Input sub-areas channel
Specifies the input image channel that contains sub-areas created by MAP. Default DBSA if no sub-areas are activated.
If MATRIX="YES" (to generate a Confusion Matrix report), this parameter must contain input training areas (bitmaps) used for classification, encoded using MAP. Each training area must be encoded with the class code for its corresponding signature segment.
InputSIG: Class signature subset 1
Optionally specifies a list of class signature segments (type 121). These segments are used only to retrieve 8-character labels for the theme classes. For classified data, these would be the class signature segments used by MLC.
Up to 256 signature segments may be handled, and up to 16 integer values may be specified.
Show Confusion Matrix
Specifies whether or not to generate a Confusion Matrix report.
Mask: Area mask
Specifies the layer that contains the bitmap mask. Only the pixels under the bitmap are processed; the rest of the image remains unchanged.
If no value is specified, the entire image is processed.
Report
Specifies where to direct the generated report.
Available options are:
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MLR creates a histogram of the number of pixels in each class in the theme channel (DBIC). In many respects, this is identical to HIS, which creates a histogram of each gray level in an input channel. The major difference is that the reports generated by MLR are laid out differently; they are more precisely labeled and they provide areas and percentages for each class.
A "theme" channel typically contains data where each pixel gray-level represents a "class". For example, gray-level 1 may be the class "corn", and gray-level 2, the class "clouds". Usually, this type of channel is output from a Classifier (MLC) or a geographic information system. Because CATALYST Professional makes no internal distinction between theme channels and imagery data (multispectral channel), however, it is possible to run any type of image channel through MLR, although the reports are designed for theme data.
If a sub-areas channel (DBSA) is specified, MLR also generates subtotalization reports. A sub-areas channel stores a theme map of up to 256 social-political regions in the image. The subtotalization reports provide a breakdown of the input theme channel (DBIC) over each social-political region. The sub-areas channel is usually generated by creating bitmaps overlaying the regions of interest, then encoding the bitmaps into an empty channel using MAP.
The MASK parameter specifies the area which will be processed. Only the area under mask will be processed; the rest of the image will remain unchanged.
The reports generated by MLR provide the totals for each class in total number of pixels, percentage coverage of the image, and area. The area will be calibrated in units (hectares, sq. feet, etc.) specified by the user (UNITS/Reporting Units). Area calculations use the pixel size specified when the database was created (using CIM, for example) or when georeferencing information was set (for example, using APS and GEOSET).
The DBS1 (InputSIG) parameter is optional. It allows reports to assign 8-character labels to the class. For each segment specified, the 8-character segment name (DBSN) and encoding value (VALU) is read. Each class in the theme map channel (DBIC) with that particular value is assigned that 8-character label in the report. For theme channels output from MLC, these segments are usually the signature segments.
Because MLR is mostly used to report on results from parallelepiped or maximum likelihood classification, class 0 is automatically labeled the "NULL" class and class 255 is labeled the "OVERLAP" class.
Overall report
The overall report is for the entire theme map channel (DBIC). This report is always generated.
Consider a 5x5 file with the first channel containing classification results produced by MLC. In this channel, class 1 is "Water", class 2 is "Corn", class 3 is "Wheat", and class 5 is "Woodland".
5 3 3 2 2
0 5 5 3 3
5 5 3 3 3
3 5 2 1 1
3 5 2 1 1
channel 1
Totalization Report for theme channel: 1
Seg Name Code Pixels UNITS %Image
xx Water 1 4 x.xx 16.00
xx Corn 2 4 x.xx 16.00
xx Wheat 3 9 x.xx 36.00
xx Woodland 5 7 x.xx 28.00
Null 0 1 x.xx 4.00
______ _________ ______
Image total 25 x.xx 100.00
Subtotalization report
If a subtotalization channel (DBSA/InputSubArea) is specified, a report is generated for each subarea on the channel.
Consider a 5x5 file with two channels. The first channel contains classification results produced by MLC. The second is the subarea channel containing two subregions (indicated by the codes 7 and 8).
5 3 3 2 2 7 7 7 8 8
0 5 5 3 3 7 7 8 8 8
5 5 3 3 3 7 7 8 8 8
3 5 2 1 1 7 8 8 8 8
3 5 2 1 1 7 8 8 8 8
channel 1 channel 2
Subarea Reports using theme channel 1 and subarea channel 2
Totalization Report for Subarea code: 7
Seg Name Code Pixels UNITS %Subarea %Image
xx Water 1 0 x.xx 00.00 00.00
xx Corn 2 0 x.xx 00.00 00.00
xx Wheat 3 4 x.xx 44.44 16.00
xx Woodland 5 4 x.xx 44.44 16.00
Null 0 1 x.xx 11.12 4.00
______ _______ ______ ______
Subarea totals 25 x.xx 100.00 36.00
Totalization Report for Subarea code: 8
Seg Name Code Pixels UNITS %Subarea %Image
xx Water 1 4 x.xx 25.00 16.00
xx Corn 2 4 x.xx 25.00 16.00
xx Wheat 3 5 x.xx 31.25 20.00
xx Woodland 5 3 x.xx 18.75 12.00
______ _______ ______ ______
Subarea totals 16 x.xx 100.00 64.00
Confusion matrix
If the MATRIX (Show Confusion Matrix) parameter is turned on (YES), and DBSA (InputSubArea) is specified, a confusion matrix report is generated. This report is based on the assumption that the values encoded in the DBSA channel correspond to the classification encoding values in the source channel (DBIC). Furthermore, it is expected that the areas in DBSA specify either the training areas for the signatures used to create the DBIC classification, or the testing areas where the user already knows the classes from reference data. If these conditions are not met, the confusion matrix report is not meaningful.
If training areas are used, the confusion matrix provides information about how much of each original training area was actually classified as being in the class that the training was meant to represent. If many pixels in the training areas are classified in classes other than those intended, it is likely that the training areas were not appropriate.
Testing areas are areas of representative, uniform land cover that are different from, and considerably more extensive than, training areas. They are often located during the training stage of supervised classification by intentionally designating more candidate training areas than are actually needed to develop the classification statistics. A subset of these may then be withheld for the post-classification accuracy assessment, again using the confusion matrix to express the results. The accuracies obtained in these areas represent at least a first approximation to classification performance throughout the scene.
An example report generated from "irvine.pix" follows. For this example, it was necessary to burn all the training areas into one channel (DBSA) using MAP.
______Areas_________ _____Percent Pixels Classified by Code______ Code Name Pixels 0 10 20 30 40 50 60 70 80 -------------------- -------------------------------------------- 10 Water1 470 0.2 96.4 0.0 2.8 0.6 0.0 0.0 0.0 0.0 20 Water2 145 2.8 0.7 89.7 6.9 0.0 0.0 0.0 0.0 0.0 30 Urban 3829 1.5 0.0 0.0 92.9 2.7 0.0 0.5 2.4 0.0 40 Range 1835 0.0 1.7 0.0 7.4 79.1 1.2 0.0 3.8 6.9 50 Crop1 1536 0.0 0.0 0.0 5.7 4.7 88.4 0.0 0.0 1.2 60 Crop2 2057 1.7 0.0 0.0 10.2 0.7 0.0 87.5 0.0 0.0 70 Crop3 350 0.0 0.0 0.0 2.0 2.6 0.0 0.0 95.4 0.0 80 Forest 1973 0.0 0.5 0.0 1.3 1.6 0.4 0.0 0.0 96.2 Average accuracy = 90.70% Overall accuracy = 90.05% Kappa Coefficient = 0.87654 Standard Deviation = 0.00336 Confidence Level : 99% 0.87654 +/- 0.00867 95% 0.87654 +/- 0.00659 90% 0.87654 +/- 0.00553
In this example, we see that of the 470 pixels in the "Water1" training area, 96.4 percent were classified as "Water1", while 0.2 percent were not classified at all (0). Looking down the matrix, we see that "Range" (40) suffered from the worst classification confusion, with only 79.1 percent of the training area classified as "Range".
The average accuracy is the average of the accuracies for each class, and the overall accuracy is a similar average with the accuracy of each class weighted by the proportion of test samples for that class in the total training or testing set. Thus, the more accurate estimates of accuracy (those from larger test samples) are weighted more heavily in the overall accuracy.
In the example above, average and overall accuracy are calculated as follows:
Average accuracy = (96.4 + 89.7 + 92.9 + 79.1 + 88.4 + 87.5
+ 95.4 + 96.2) / 8
Overall accuracy = (0.964x470 + 0.897x145 + 0.929x3829 +
0.791x1835 + 0.884x1536 + 0.875x2057 +
0.954x350 + 0.962x1973) /
(470 + 145 + 3829 + 1835 + 1536 + 2057 +
350 + 1973)
The Kappa coefficient, standard deviation, and confidence levels are also included in the report.
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