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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

10/13/2025

On July 24, 2025, a unique international partnership of SaraniaSat, NV5 Geospatial Software, BruhnBruhn Innovation (BBI), Netnod, and Hewlett Packard Enterprise (HPE) achieved something unprecedented: a true demonstration of cloud-native computing onboard the International Space Station (ISS) (Fig. 1). Figure 1. Hewlett... Read More >

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The minimum and maximum operators

Anonym

Have you used the minimum (<) and maximum (>) operators in IDL? They're among my favorites; they're binary operators that return the smaller (minimum) or larger (maximum) of their operands. For example, 5 is larger than 2, so the minimum operator returns 2:

IDL> print, 5 < 2
       2

whereas the maximum operator returns 5:

IDL> print, 5 > 2
       5

Note that these differ from the IDL relational operators LT and GT, which are also binary operators, but instead return a true (1B) or false (0B) value:

IDL> print, 5 lt 2
   0
IDL> print, 5 gt 2
   1

It gets better: like most operators in IDL, the minimum and maximum operators also work on arrays. For example, a sill operation raises the minimum value of an array to the value of the sill. Here, the array new is created by applying the maximum operator to the array test with the value sill:

IDL> test = indgen(5)
IDL> sill = 2
IDL> print, test
       0       1       2       3       4
IDL> new = test > sill
IDL> print, new
       2       2       2       3       4

Note that no value of new is less than sill. The minimum and maximum operators can also be used in masking. Imagine data, stored in an array, that has bad values. I want to mask out and replace the bad values. For this example, I'll use RANDOMN to generate sample data:

IDL> sample = randomn(1, 5, 5)
IDL> print, sample
    -0.836854    -0.172280     0.187117      1.61544    -0.176774
     0.653145    -0.546364     0.194146     0.925709      1.20432
      1.53055     -1.35556    0.0514889      1.02018     -1.22616
     0.708497     0.871673    -0.789721     0.332079     0.205603
    -0.169367    -0.318417    -0.295643     0.522291     -2.23105

Say the bad values are less than zero and I want to replace them with the value zero. You could use the WHERE function to identify the locations of these values in the array:

IDL> i_bad = where(sample lt 0.0, /null)
IDL> help, i_bad
I_BAD           LONG      = Array[11]

and then, by subscripting, replace them with zeros:

IDL> sample[i_bad] = 0.0
IDL> print, sample
     0.000000     0.000000     0.187117      1.61544     0.000000
     0.653145     0.000000     0.194146     0.925709      1.20432
      1.53055     0.000000    0.0514889      1.02018     0.000000
     0.708497     0.871673     0.000000     0.332079     0.205603
     0.000000     0.000000     0.000000     0.522291     0.000000

But the maximum operator, used this time in a compound operator, provides a one-line solution:

IDL> sample = randomn(1, 5, 5) ; reconstitute the sample
IDL> sample >= 0.0
IDL> print, sample
     0.000000     0.000000     0.187117      1.61544     0.000000
     0.653145     0.000000     0.194146     0.925709      1.20432
      1.53055     0.000000    0.0514889      1.02018     0.000000
     0.708497     0.871673     0.000000     0.332079     0.205603
     0.000000     0.000000     0.000000     0.522291     0.000000

Cool! Do you have an example in your work where you use the minimum or maximum operator?

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