Delivered Ideas

Discuss topics with other Hexagon Geospatial Product pioneers and experts to get the most out of our products.
Showing results for 
Search instead for 
Do you mean 
New Idea

WMTS support for ERDAS IMAGINE

Status: Delivered
by on ‎01-18-2019 02:47 AM

Hi there,

 

I would like to see WMTS support within ERDAS IMAGINE. WMS as well as WCS are supported via Open Web Service but not WMTS. Some services (e.g. coming from www.basemap.at) are only available as WMTS and not as WMS.

 

Thanks for getting this into the product.

 

Cheers

Fritz

 

Status: Delivered

 

File > Open > Web Service.. supports WMTS with the ERDAS IMAGINE 2020 release

Sentinel 2- Level 2 BOA

Status: Delivered
by on ‎09-27-2018 09:47 AM - last edited on ‎09-27-2018 10:30 AM by Technical Evangelist

Dear all,

 

The new Sentinel 2 - Level 2, available in the Sci-Hub, at the moment are not supported by ERDAS. I have found a workaround. 
But will this be managed in the next versions? thank you

Regards

Status: Delivered

 

Sentinel-2 Level 2A data is now supported by ERDAS IMAGINE 2020

As an ERDAS IMAGINE user attempting to perform supervised classification I would like to be able to run GLCM filters (also sometimes known as Haralick Textures) in order to create additional layers of texture measures for my data.

 

 

ERDAS ER Mapper already implements over a dozen different GLCM filters:

 

https://hexagongeospatial.fluidtopics.net/reader/aBeAmMF1rSQjl~wOD6IDZA/wLx87ePDTy0AacBYuYhd4g

 

Seen as important inputs to supervised classification:

 

  • Angular Second Moment (ASM)
  • Contrast (CON)
  • Correlation (COR)
  • Entropy (ENT2)
  • Homogeneity (HOM)
  • Etc.
Status: Delivered

 

A "Compute GLCM Texture" operator is provided as part of Spatial Modeler 2020. This operator supports GPU acceleration.

Traditionally, ERDAS IMAGINE (and the SMSDK) have been tested and declared Supported on Server versions of Microsoft Windows, primarily in support of "headless" operations such as distributed processing and/or background batch processing (but some people also use the full GUI), as well as to provide compatibility for ERDAS APOLLO and other applications. Microsoft plans to release Server 2019 later in Calendar Year 2018 and so it would be good to see ERDAS IMAGINE supported on that environment.

 

https://cloudblogs.microsoft.com/windowsserver/2018/03/20/introducing-windows-server-2019-now-availa...

Status: Delivered

 

ERDAS IMAGINE 2020 is considered supported on the Windows Server 2019 operating system.

Support for Windows 10 Enterprise

Status: Delivered
by on ‎10-11-2019 12:12 PM

Provide support for Windows 10 Enterprise 64-bit, used in many corporate environments.

Status: Delivered

 

Windows 10 Enterprise (64-bit) is now listed as a supported operating system for ERDAS IMAGINE 2020 Update 1 and the Release Guide's System Requirements section has been updated to reflect this:

 

ERDAS_IMAGINE_2020_Release_Guide

 

 

Direct write to BigTIFF

Status: Delivered
by Technical Evangelist on ‎02-07-2018 11:05 AM

As a user of very large image files I would like to have the capability to directly create BigTIFF from Spatial Modeler, MosaicPro, Viewer Save As, etc. This would enable the direct creation of very large (greater than 4GB) image files in a common exchange format.

 

Currently I have to create the large files in another format (such as IMG or lossless JPEG 2000) and then export to BigTIFF.

Status: Delivered

 

Direct write to BigTIFF is now supported in ERDAS IMAGINE 2020. Spatial Modeler (and applications which use it) only.

Preview Window Provides Real Data Values

Status: Delivered
by on ‎09-28-2017 01:10 AM

I am a user creating a thresholded NDVI dataset using Spatial Modeler. I would like to examine the real data values in order to ascertain the correct threshold value. Currently, I need to produce an output dataset in order to do this. It would be better if the Preview window allowed me to interogate the real values of the data rather than just being a dumb visualisation.

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

 

New atmospheric correction algorithm

Status: Delivered
by Moderator on ‎08-21-2017 05:12 AM - last edited on ‎08-30-2017 06:51 AM by Technical Evangelist

Build on the algorithms implemented in the Rapid Atmospheric Correction spatial operator, to add a new spatial operator which enables any 16-bit imagery with at least four bands in the wavelength range from Coastal Blue to NIR2 to be atmospherically corrected to ground reflectance based on parameters that can be derived from the image header. Correcting to ground reflectance has the advantage of normalizing scene-to-scene variations, which in turn makes tasks such as change detection, standardized classification, and other feature extraction tasks more straightforward.

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

Sensor Independent Complex Data (SICD) interpretation and processing

Status: Delivered
by Moderator on ‎08-21-2017 05:11 AM - last edited on ‎10-05-2017 12:03 PM by Technical Evangelist

Modern SAR imagery is often distributed in NITF format with SICD geometry information. ERDAS IMAGINE should directly read SICD imagery as well as provide processing options such as orthocorrection and interferometric processing

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

As the Spatial Modeler user, I would like all attributes from the thematic raster to be transferred to the shapefile's attribute table via the Convert to Features operator. Currently, we have the workaround that is to run rastertoshape.exe via Command Line. It's not the best solution, especially if the model will be further used as a recipe in Smart M.Apps, for example. 

 

The good bonus here would be also transferring color table of thematic raster to a shapefile's symbology file (evs). 

Status: Delivered

 

Delivered as part of the Spatial Modeler 2020 release.

Some lidar vendors make by default ground etc. classification. For example Swedish NLS provides ground and water. Finnish NLS provides ground and some vegetation. Rest is unclassified. My assumption is that they classify automatically such things they can trust for sure and leave rest empty.

 

Erdas Imagine Pointcloud classifier makes always a full classification from scratch. So everything is always reclassified. I am not sure can we produce better result for those that someone else has already made. So are we really improving or decreasing result a bit?

 

I have a decent workaround by using spatial modeller and select only unclassified points to classification. That works pretty OK but fails as not all points are included in process. So I can do the thing I want but only with subset of points which decreases the output quality. What I would like to do is full classification using all points in dataset but new values written just points that had no classification before.

 

So simple tick in lidar point cloud classifier - classify only unclassified points. That is my request in compressed form.

 

As an attcahment simple sample model how to work it out but as told it has its weak points too - mainly for decresing the data used and by that compromizing the output quality a bit

Status: Delivered

 

Delivered as part of the Spatial Modeler 2020 release.

Request ERDAS Imagine and related products - including Photogrammetry - support New Zealand Vertical Datum 2016 (NZVD2016) and the13 local vertical datums.

 

Information can be found at

https://www.linz.govt.nz/data/geodetic-system/datums-projections-and-heights/vertical-datums for summary of NZ vertical datums

https://www.linz.govt.nz/data/geodetic-system/datums-projections-and-heights/vertical-datums/new-zea... (has attachment NZGeio2016 SID file for download).

Status: Delivered

 

Installing Imagine Objective on Imagine 16.5 needs a different configuration from IMAGINE 16.1 and there are a couple of things we need to set up manually:

- make sure that both the 32-bit and 64-bit Java versions are installed in the correct Win32Release and X64URelease sub-folders. -JAVA_HOME system variable must be set to the top folder where Java is installed.

 

To prevent the message "Error initializing classifier!" and Imagine crash after opening Imagine Objective if the attended configuration is not set on the machine, an improved warning message would  be usefull, helping users to correctly configure Java for the IO application.

Status: Delivered

 

Delivered as part of the Spatial Modeler 2020 release.

 

If Java has not been configured you will receive a warning with the text below, and the software will not crash:

 

Error initializing classifier!
Bayesian classifier requires a Java Runtime Environment, but
none was found! Consult the Configuration Guide for installation
instructions.

0 HexPoints

Default Sensor on Multispectral Tab

Status: Delivered
by on ‎07-02-2018 07:47 AM

As a user of Landsat data, nowadays I most commonly use Landsat 8. However, when I load this into IMAGINE the default sensor on the Multispectral tab is Landsat 4. Can the sensors be rearranged so that it is the latest sensor that is the default, as a proxy for the most commonly used data. Also applies to WorldView, SPOT etc. etc. Ta.

Status: Delivered

 

Delivered as part of the Spatial Modeler 2020 release. 7 band Landsat 8 MSI images now default to Landsat 8 on the Sensor menu.

0 HexPoints

NNDiffuse Pan Sharpening

Status: Delivered
by Technical Evangelist ‎08-30-2017 07:08 AM - edited ‎10-12-2017 07:44 AM

The Nearest-neighbor diffusion-based (NNDiffuse) algorithm, originally developed by Sun, Chen and Messinger at Rochester Institute of Technology, is a state of the art pan sharpening technique. Looks like an excellent option to implement as a new Spatial Modeler Operator.

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

0 HexPoints

Use your own change algorithm in Zonal Change Detection

Status: Delivered
by Moderator on ‎08-21-2017 05:11 AM - last edited on ‎08-30-2017 06:44 AM by Technical Evangelist

Change the architecture of Zonal Change detection to accept custom change detection algorithms. You can now use your own algorithms that better detect changes that are of interest to you. pProvide several optional algorithms to choose from.

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

0 HexPoints

Machine learning operators

Status: Delivered
by Moderator on ‎08-21-2017 05:14 AM - last edited on ‎08-24-2017 07:41 AM by Technical Evangelist

Implement Classification algorithms based on machine learning as Operators in Spatial Modeler. They can be used to perform multi-class prediction.

 

Options might include

 

Classifier

Algorithm

When to use

CART Decision Tree

Decision Trees use a chain of simple decisions based on the results of sequential tests for class label assignment. The branches of the DT are composed of sets of decision sequences where tests are applied at the nodes of the tree and the leaves represent the class labels.

·        Simple and easy to understand

·        less influenced by outliers so good for classifying noisy data.

K Nearest Neighbors

Find the K nearest neighbors of each point, and assign the most occurring class to the point

·        Good for uniformly sampled data.

Logistic regression

 

·        This is the go-to method for binary classification problems (Yes/No)

·        It is affected by noise. So a clean training data is needed.

Naive Bayes

A classification technique based on Bayes’ Theorem with an assumption of independence among predictors. Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

·        When the assumption of independence holds, a Naive Bayes classifier performs better compared to other models and requires less training data.

Radius Neighbor

Find the neighbors within a fixed radius each point, and assign the most occurring class to the point

·        Better choice than K Nearest Neighbors when data is not uniformly sampled

Random Forest

In Random Forest, we grow multiple trees as opposed to a single tree in CART model.  Each tree gives a classification and we say the tree “votes” for that class. The forest chooses the classification having the most votes

·        Multiple trees as opposed to a single tree in CART model

·        considered to be a panacea of all data science problems. Generally start with this classifier and evaluate if the results are appropriate.

Support vector Machine (SVM)

Support Vector Machine (SVM) performs classification by constructing hyperplanes in a multidimensional space that separates different class,

·        Works extremmely well with clear margins of separation. Don't use if the target classes are overlapping

·        Does not perform well on highly skewed/imbalanced training data sets.

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

0 HexPoints

Advanced point cloud processing such as Height above Ground

Status: Delivered
by Moderator on ‎08-21-2017 05:12 AM - last edited on ‎08-29-2017 01:02 PM by Technical Evangelist

This makes it easier to create flooding, building height, and forest canopy models based on point cloud data.

 

Take a regular point cloud, with heights measured as height above mean sea level, and subtract a second surface representing the ground surface. The result would represent height above ground level (e.g. heights of trees, buildings, etc).

 

This would enable building Spatial Models to, for example,

 

  1. Acquire point cloud
  2. Classify point cloud to identify tall vegetation (i.e. trees) and ground (bare earth) points
  3. Interpolate ground points to continuous raster ground surface
  4. Subtract ground surface from all points
  5. Use Zonal operators to attribute forest stand polygons with average tree height per polygon
  6. Manager determines which stands are ready for harvest

 

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

0 HexPoints

Feature extraction operators

Status: Delivered
by Moderator on ‎08-21-2017 05:12 AM - last edited on ‎08-30-2017 06:50 AM by Technical Evangelist

Port IMAGINE Objective functionality into Spatial Modeler.

 

Several IMAGINE Objective functionalities (such as classifiers, raster cues, vector cues, vector cleanup operators) should be made available as operators so you can use them to build feature extraction workflows. Using the extensibility of Spatial Modeler, you can also create and use your own classifiers or cues to fit your specific application.

Status: Delivered

 

A large selection of such Operators have been delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018

0 HexPoints

Exploit multi-segment NITF data

Status: Delivered
by Moderator on ‎08-21-2017 05:12 AM - last edited on ‎08-30-2017 06:46 AM by Technical Evangelist

Modern NITF data can consist of multiple image segments, such as pixel quality information, cloud cover, and multiple tiles of a single image. Need a way to access and exploit this data much more easily.

Status: Delivered

 

Delivered as part of ERDAS IMAGINE 2018, released March 1st, 2018

 

https://download.hexagongeospatial.com/downloads/imagine/erdas-imagine-2018