#Tntmips compact ratio license

The default name of the distance raster begins with 'DST' followed by the Method name.

manually add the distance raster you created in the previous exercise (via Add Layer).

The following steps are done in the Classification View window. Zoom in to an area of interest, then use the Show/Hide checkbox for layers in Sidebar to compare the input rasters with the resulting class and distance rasters. (continued from previous exercise) Exercise 3 - Compare classes to input layer and distance raster You can re-load the resulting class raster using the Open icon to use the same results in later exercises. rvc file where you put the class and distance rasters. Make a note of the file path and name of the output. rvc file, and accept the default object names for them.

when prompted navigate to the folder you want to put the output rasters, create a new.

Red:B6, Green:B5, Blue:B4 with Contrast:Auto Normalize)

set up how the newly added multiband raster layer is to be displayed via Raster Layer Controls (i.e.

click the New icon on the Automatic Image Feature Classification window and add all of the raster bands in the stanton_landsat8.rvc file (or least one band from your own multispectral data).

select Image > Classify > Auto-Classify from the TNTmips menu bar.

To try a quick run through of the process all you need to do is select the input, choose an unsupervised classification method, set the number of classes, and run it.

Remove outliers from a class via distance histogram.

Compare classes plotted in 2D spectral space.

Analyze classes with coocurrence / separation matrix.

Analyze related classes using the dendrogram and View window.

Compare classes to input layer and distance raster.

Unless otherwise indicated, subsequent lessons can be done in any order independently from the reading. The first three lessons provide a quick introduction. Cells with the value of 0 were set to null.) Tutorial Lessons:

#Tntmips compact ratio license

(A portion of a Landsat 8 scene was extracted to fit within the size constraints of the TNTmips Free license level.

Prerequisite Skills: Displaying Geospatial Data and TNT Product Concepts. After running the classification process, various statistics and analysis tools are available to help you study the class results and interactively merge similar classes. Unsupervised methods automatically group image cells with similar spectral properties while supervised methods require you to identify sample class areas to train the process. The Classification process offers a variety of classification methods as well as tools to aid in the analysis of the classification results. The relationship between spectral classes and different surface materials or land cover types may be known beforehand or determined after classification by analysis of the spectral properties of each class.

This process uses the spectral pattern (or 'color') of a raster cell in multispectral or multi-temporal imagery to automatically categorize all cells into spectral classes. The TNTmips ® Automatic Image Feature Classification process automatically groups image cells with similar spectral properties into classes.