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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!



Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

Not All Supernovae Are Created Equal: Rethinking the Universe’s Measuring Tools

6/3/2025

Rethinking the Reliability of Type 1a Supernovae   How do astronomers measure the universe? It all starts with distance. From gauging the size of a galaxy to calculating how fast the universe is expanding, measuring cosmic distances is essential to understanding everything in the sky. For nearby stars, astronomers use... Read More >

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

Using LLMs To Research Remote Sensing Software: Helpful, but Incomplete

5/26/2025

Whether you’re new to remote sensing or a seasoned expert, there is no doubt that large language models (LLMs) like OpenAI’s ChatGPT or Google’s Gemini can be incredibly useful in many aspects of research. From exploring the electromagnetic spectrum to creating object detection models using the latest deep learning... Read More >

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

From Image to Insight: How GEOINT Automation Is Changing the Speed of Decision-Making

4/28/2025

When every second counts, the ability to process geospatial data rapidly and accurately isn’t just helpful, it’s critical. Geospatial Intelligence (GEOINT) has always played a pivotal role in defense, security, and disaster response. But in high-tempo operations, traditional workflows are no longer fast enough. Analysts are... Read More >

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

Thermal Infrared Echoes: Illuminating the Last Gasp of a Dying Star

4/24/2025

This blog was written by Eli Dwek, Emeritus, NASA Goddard Space Flight Center, Greenbelt, MD and Research Fellow, Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA. It is the fifth blog in a series showcasing our IDL® Fellows program which supports passionate retired IDL users who may need support to continue their work... Read More >

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

A New Era of Hyperspectral Imaging with ENVI® and Wyvern’s Open Data Program

2/25/2025

This blog was written in collaboration with Adam O’Connor from Wyvern.   As hyperspectral imaging (HSI) continues to grow in importance, access to high-quality satellite data is key to unlocking new insights in environmental monitoring, agriculture, forestry, mining, security, energy infrastructure management, and more.... 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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