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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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Subsetting ENVIRasters for ENVITask

Anonym

A few months ago I wrote about ENVITask, the new API for analysis in ENVI 5.1.  Anyone who has looked at this API will notice that it the task objects do not contain parameters for spatial or spectral subsetting like ENVI_DOIT does with POS and DIMS.  Adding these parameters to every task causes unnecessary internal complication, and isn't needed due to our "raster is a raster" philosophy.  If you only want to run a task on a subset of the raster, it's up to you to perform the subsetting before invoking the task.  This is accomplished with the ENVIRaster::Subset() method, which returns a new ENVIRaster object that provides access to only that subset.

 

As the docs describe, there are three forms of subsetting that can be performed:

 
  • spatial subsetting using SUB_RECT
  • spectral subsetting using BANDS
  • pixel masking using ROI

 

You can use any or all of these keywords together, though if you specify by SUB_RECT and ROI the spatial subsetting is performed first, then the ROI masking is done.

 

Spatial subsetting is performed using the SUB_RECT keyword, which is expressed in pixel coordinates as an array [ leftCol, topRow, rightCol, bottomRow ].  These values will be clamped to the raster if you specify negative values or values too large.

 

Spectral subsetting is performed using the BANDS keyword, which is either a scalar band index or an array or band indices.  These indices are 0-based, and can't be repeated, but the order of them is significant and can be used for band reordering.  So setting BANDS=[0,1,2] will give you a different output raster than BANDS=[2,1,0].  Out of range band indices will throw an error.

 

Pixel masking using an ENVIROI is performed using the ROI keyword, which is set to the ENVIROI object reference.  This won't change the spatial or spectral extents of the raster, but it will mask out any pixels that aren't inside the ROI.  If you're writing your own extensions or processing scripts, you have to use the PIXELSTATE keyword when you call ENVIRaster::GetData() or ENVIRasterIterator::GetData().  As the documentation of these two methods explains, this keyword will be set to a byte array of the same dimensions as the data, which uses a bitmask to tell you whether each pixel is valid or not and why not.  For most purposes, the only question is whether the PIXELSTATE value is 0 or not, where 0 means valid and any non-zero value means invalid.

 

Here is an example that loads a Quickbird mosaic, chips out a small spatial subset with only the visible bands and calibrates it to a radiance image, and then loads the resulting output in a raster layer to compare to the original raster:

 

; load the raster

nv = ENVI()

inputFile = Dialog_Pickfile(TITLE='Select a file to calibrate')

oRaster = nv.OpenRaster(inputFile)

 

; display the raster in view

oView = nv.GetView()

oLayer1 = oView.CreateLayer(oRaster)

 

; subset the raster for task processing

subRect = [700, 900, 1000, 1300]

bands = [0, 1, 2]

oInput = oRaster.Subset(SUB_RECT=subRect, BANDS=bands)

 

; load the enviTask

oTask = enviTask('RadiometricCalibration')

 

; Set parameters

oTask.INPUT_RASTER = oInput

oTask.CALIBRATION_TYPE = 0 ; Radiance

oTask.OUTPUT_DATA_TYPE = 4 ; Float

; run the task

oTask.Execute

; display the results

oLayer2 = oView.CreateLayer(oTask.Output_Raster)

 

A screen shot of this code shows the results:

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