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The color of the scatterplot matches the color of the class in Training Sample Manager.To compare the distributions of two or more training samples, select the classes represented by the training samples in Training Sample Manager and click the Scatterplots button.If the training samples represent different classes, their scatterplots should not overlap. The Scatterplots window is another way to compare multiple training samples. The following image shows an example of the Histograms window: The Histograms evaluation window The Scatterplots window If a class is hidden in the background in one or more histograms, you can bring it to the foreground by clicking on this button. The Change Order button (located at the bottom of the window) allows you to change the overlapping order of the histogram series.If the image data is stored as floating point, the Histograms button will be unavailable on Training Sample Manager. This tool works for integer images only.If you have more than four bands in the image layer (which means there are more than four graphs), a vertical scrollbar will be available. This window displays four graphs in one screen.The Histograms window contains the same number of graphs as the number of bands in the image layer.The color of the histogram matches the color of the class in Training Sample Manager.To compare the distributions of two or more training samples, select their classes in Training Sample Manager and click the Histograms button.If the training samples represent different classes, their histograms should not overlap each other. The Histograms window allows you to compare the distribution of multiple training samples. These tools are accessible as buttons on the manager. To check the separability and distribution of your training samples, Training Sample Manager provides three evaluation tools: a Histograms window, a Scatterplots window, and a Statistics window. If any of them are overlapping, you might consider merging them into one class. Different classes should be separated in the multidimensional attribute space. Training samples are created to represent classes in a supervised classification.