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

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

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

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

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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4.5

What's New in ENVI Deep Learning? A Lot.

Zachary Norman Monday, February 26, 2024

Introduction

We are excited to announce the release of ENVI® Deep Learning 3.0. This version of the software not only marks a significant milestone in our journey but sets a new benchmark in deep learning for remote sensing. ENVI DL 3.0 is accessible through our downloads portal, however it’s important to note that harnessing its full potential requires the latest release of ENVI, version 6.0. Together, they form a powerhouse duo, unlocking a suite of capabilities poised to transform how we interact with imagery.

The Flagship Feature: Grid Models

At the heart of ENVI Deep Learning 3.0 is a new type of model: grids. This innovative approach uses a specialized deep learning model to dissect images into a grid, pinpointing the exact location of target features with unprecedented accuracy.

An example of grids detecting clouds in WorldView imagery.

An example showing where aircraft and helicopters might be.

One of the nicest things about grid is that you don’t need to label data any differently than you already do today! This feature allows you to leverage existing training data for object detection and pixel segmentation models, making grid models ready to use from day one.

The Grid Advantage: Speed and Optimization

You might be thinking “grid models are neat, but why should I care?” The answer is in their unmatched efficiency. Grid models can process images in a fraction of the time required by traditional segmentation or object detection models. This speed, combined with our existing models, enable a patent-pending deep learning pipeline that amplifies the value of grids.

Using our optimized classification tools, you can:

Reduce false positives, to simplify manual efforts to correct deep learning results.
Save time, which improves productivity and reduces cost for cloud-based processing.
Improve throughput of hardware-limited applications that cannot automatically scale to meet processing demands.
Quickly use more complex models and architectures that can better detect unique features, such as the new TensorFlow Optimized Object Classification and TensorFlow Optimized Pixel Classification.

Migrating to ENVI Deep Learning 3.0

One of the reasons that we bumped the version to 3.0 is that there are some breaking changes with our deep learning APIs. To facilitate a smooth transition, we’ve prepared an in-depth migration guide that is included with the product that helps walk you through:

Migrating ENVI Modeler workflows
Migrating IDL® code using the ENVI Deep Learning API

If you have IDL code that needs to be migrated, you can use IDL for VSCode to run the command “IDL: Migrate Code to Deep Learning 3.0 API” and make changes automatically.

ENVI Connect UI

Conclusion

The release of ENVI DL 3.0 is not just an update, it helps bring our deep learning capabilities into the future of remote sensing.

There’s a lot of directions this can go and ways it can be used, including integrated deep learning with ENVI Connect. For those that don’t know, ENVI Connect is an enterprise solution to help teams of analysts work with imagery collaboratively and derive meaningful information.

In the image above, features were automatically detected with ENVI Deep learning 3.0 in 30 seconds. Without grids, the deep learning model used to take 12 minutes to process this scene. With grids it goes from “take a break or multitask” to “have a few sips of coffee and you have your “answer", turning downtime into productivity.

If you are interested in learning more about ENVI DL 3.0 or trying it for yourself, simply send us an email.