>  Docs Center  >  Using ENVI  >  Create Binary Rasters by Automatic Thresholds

Create Binary Rasters by Automatic Thresholds

Create Binary Rasters by Automatic Thresholds

Use the Binary Raster by Automatic Threshold tool to create a binary image using a predefined thresholding method. Thresholds are calculated for each band in the source image. Image thresholding is typically done to separate "object" or foreground pixels from background pixels to aid in image processing.

You can write a script to create binary rasters by automatic thresholds using ENVIBinaryAutomaticThresholdRasterTask.

Follow these steps:

  1. From the Toolbox, select Raster Management > Binary Raster by Automatic Threshold.
  2. Select a single-band or multi-band Input Raster, and perform optional spatial and/or spectral subsetting.
  3. Select a thresholding method from the Method drop-down list. The choices are:
    • Isodata: This method works iteratively by calculating an initial threshold that is half the dynamic range of the image or layer, effectively dividing the image into "foreground" (above the initial threshold) and "background" (below the initial threshold) pixels. Next, the algorithm separately calculates the sample mean of the foreground and background pixels, using these new sample means to calculate a new threshold value (the average of the sample means). The process repeats using each new, successive threshold value until the resulting threshold value ceases to change (Ridler and Calvard, 1978).
    • Mean: This method takes the mean value of the gray levels as the threshold (Glasbey, 1993).
    • Maximum Entropy: This method considers the thresholding image as two classes of events, with each class characterized by a Probability Density Function (PDF). It then maximizes the sum of the entropy of the two PDFs to converge on a single threshold value (Kapur, Sahoo, and Wong, 1985).
    • Minimum Error: This method approximates the histogram as a bimodal Gaussian distribution and finds a cutoff point. The cost function is based on the Bayes classification rule (Kittler and Illingworth, 1986).
    • Moments: This method considers the grayscale image as a blurred version of an ideal binary image. This method determines the threshold so that the first three moments of the input image are preserved in the output image (Tsai, 1985).
    • Otsu (default): A histogram shape-based method. It is based on discriminate analysis and uses the zero- and the first-order cumulative moments of the histogram for calculating the value of the thresholding level (Otsu, 1979).
  4. The Inverse option is set to No by default, which means that values above the computed threshold are set to 1 and all other values are set to 0. Set this option to Yes to invert the values.
  5. To write the output to disk, select the File radio button and specify a filename and location. To produce output in memory only, select the Virtual radio button.

  6. Enable the Preview check box to see a preview of the settings before you click OK to process the data. The preview is calculated only on the area in the Image window and uses the resolution level at which you are viewing the image. See Preview for details on the results. To preview a different area in your image, pan and zoom to the area of interest and reenable the Preview option.
  7. Enable the Display result check box to display the mask in the Image window when processing is complete. Otherwise, if the check box is disabled, the mask can be loaded from the Data Manager.
  8. To run the process on a local or remote ENVI Server, click the down arrow and select Run Task in the Background or Run Task on remote ENVI Server name. The ENVI Server Job Console will show the progress of the job and will provide a link to display the result when processing is complete. See the ENVI Servers topic for more information.

  9. Click OK. ENVI adds the resulting output to the Data Manager and, if the Display result check box was enabled, adds the layer to the Layer Manager and displays the output in the Image window.


Glasbey, C. "An Analysis of Histogram-Based Thresholding Algorithms." CVGIP: Graphical Models and Image Processing 55 (1993): 532-537.

Kapur, J., P. Sahoo, and A. Wong. "A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram." Graphical Models and Image Processing 29, No. 3 (1985): 273-285.

Kittler, J., and J. Illingworth. "Minimum Error Thresholding." Pattern Recognition 19 (1986): 41-47.

Otsu, N. "A Threshold Selection Method from Gray-Level Histograms." IEEE Transactions on Systems, Man and Cybernetics 9 (1979): 62–66.

Ridler, T., and S. Calvard. "Picture Thresholding Using an Iterative Selection Method." IEEE Transactions on Systems, Man and Cybernetics 8 (1978): 630 - 632.

Tsai, W. "Moment-Preserving Thresholding: a New Approach." Computer Vision, Graphics, and Image Processing 29 (1985): 377-393.

© 2020 Harris Geospatial Solutions, Inc. |  Legal
My Account    |    Store    |    Contact Us