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Spatial Model Library List
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Sterling Geo's Spatial Modeler Library

Clump and Sieve Sub Model v2

Required input: Binary raster

This is an updated model of the Clump and Sieve Sub Model using ERAS IMAGINE's Spatial Modeler v2016. This model combines the clump and sieve functions designed for use on a thematic dataset. First, clump identifies contiguous groups of pixels, then sieve removes the clumps smaller than a user defined size.

Watch this month's two minute tip, showing how to use this model, available here.

Download as ready to run.

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Fill Cloud Holes in Classification

This model takes a classification with cloud holes and fills in the holes with values from another classification of another date. This model removes the need to manually map classification values prior to combining. Therefore, two cloud masked unsupervised classification outputs can be added straight into this model. Watch this month's two minute tip, showing how to use this model, available here.

Download as ready to run.

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Subset by Shapefile Model

Required input: A raster image and a shapefile area of interest

This model subsets raster imagery by a Shapefile polygon, the input imagery will be clipped by the polygon extent. This model is useful for reducing datasets down to your area of interest. Please note, this model will only work in ERDAS IMAGINE v2016. Watch this month's two minute tip explaining how to use this model, available here.

Download as ready to run.

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Subset by Shapefile Model

Required input: A raster image and a shapefile area of interest

This model subsets raster imagery by a Shapefile polygon, the input imagery will be clipped by the polygon extent. This model is useful for reducing datasets down to your area of interest. Please note, this model will only work in ERDAS IMAGINE v2016. Watch this month's two minute tip explaining how to use this model, available here.

Download as ready to run.

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NDVI Model Using Apply Index Operator

This model demonstrates how to create an NDVI using the new Apply Index operator. Please note, this model can only be used with ERDAS IMAGINE v2016.

Watch this month's two minute tip explaining how to use this model, available here.

Download as ready to run.

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Dark Object Subtraction

This model demonstrates the use of the iterator operator. Watch February's two minute tip for more information, available here. Please note, this model can only be used with ERDAS IMAGINE v2016.

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Automatic Naming of Output Files Add-On Model

This model obtains the Input data name and directory, then appends '_output.img', onto the end of the filename, to specify an automatically generated output name. This new capability in 2016 enables you to save time when running large volumes of data in batch. Please note, this model can only be used with ERDAS IMAGINE v2016.

Watch December's two minute tip explaining how to use this model, available here

Download as ready to run.

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Multi-Hill Shade Creator

Required input: DEM file (for example environment agency Lidar data)

Mutli-Hill Shade creator for visualising small changes in elevation. This may be useful for archaeological applications. Please note, this model can only be used with ERDAS IMAGINE v2016.

Download as ready to run.

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Raster Buffer Around a Binary Image

Required input: Binary image (or it can be adapted to use a thematic image, searching around a particular pixel value)

This model utilises the Search operator to extend any areas with a pixel value of 1 by a specified number of pixels. An example use case for this model is extending a cloud mask to take out hazy edges.

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Active Fire Detection Using SWIR and NIR Band

Required input: Imagery which contains a SWIR band and NIR band such as, Landsat or Sentinel-2

This model runs a simple ratio on SWIR and NIR data to highlight active fires. Active fires have a very distinctive response in SWIR, which allows easy detection.

Download as ready to run.

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Sentinel-2 Cloud Removal Model

Required input: Sentinel-2

A model which thresholds band 1 of Sentinel-2 imagery to mask out cloud in your scene. You may need to adjust the greater than threshold.

Download as ready to run.

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Sentinel-2 NDVI Model

Required input: Sentinel-2

This model generates a NDVI from the new Sentinel-2 imagery, producing a 10m resolution NDVI image by utilising band 8 and band 4.

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Sentinel GRD 8-bit Colour Polarimetric Model

Required input: Sentinel-1 GRD (Ground Range Detected) VV and VH tiffs

This model takes the two polarised datasets from Sentinel-1 and creates a three band output; VV, VH and VV/VH. It rescales the output to 8 bit and applies a simple smoothing filter to try and cut down on the speckle. The output is a colour image that is a little more interpretable to the casual user than the individual bands on their own.

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Using ERDAS IMAGINE to Import .asc Files

Required input: Python must be installed for this model and the input data must be .asc format

This model adds to ERDAS IMAGINE’s current capability, allowing it to transform .asc format into .img. The model will run through all the .asc files in the specified folder.

GDAL DLLs are included in this spatial model.

Download as ready to run.

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Importing MODIS with NDVI Rescale

Required input: MODIS data - VEGETATION INDICES 250M/500M TERRA

This model takes MODIS NDVI .hdf format data as supplied by USGS. The model extracts the NDVI band, saves to .img and rescales the data to standard NDVI pixel values of between -1 and 1, allowing quick processing of MODIS data. Please note the model will not work with V2013.

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Categorised NDVI

Required input: Dataset with visible red and near infra-red bands

This model creates an NDVI dataset, categorises it into 6 levels and then outputs to a thematic dataset with colour and NDVI attributes. That's good in itself, but it also demonstrates how to attach more than one attribute to an output thematic raster using the create columns function.

Download as ready to run, or in an editable format.

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Raster to Shapefile Operators

Required input: Thematic Raster File

Although there is no dedicated operator for converting a raster to shapefile in Spatial Modeler, this is possible using the command line function.

This model demonstrates the concept. In order to convert from raster to vector, you do need to specify an output raster file (Thematic Output) in the model; you can't just go from a function directly to the command line operator.

Adjustments to the Model:

If the shapefile is your final output, you can remove the Vector Input operator from the model. You can also remove the Output vector filename port input and directly specify a filename in the rastertoshape operator.

Many thanks to Partha Roy at Intergraph who helped out substantially with this process.

Download as an editable format.

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Clump and Sieve Sub Model

Required input: Thematic Raster File

This model combines the clump and sieve functions designed for use on a thematic dataset. First, clump identifies contiguous groups of pixels, then sieve removes the clumps smaller than a user defined size.

Download as ready to run.

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Area Values from a Thematic Raster Input

Required input: Thematic Raster File

This model generates an area value for a thematic raster dataset. This model can be added to the end of another model after a criteria function (or another operator which outputs a thematic raster) to receive a table file output of the area values. Or you can directly import a thematic raster to find the area values of the zones.

The area values are currently in square miles, this can be edited to any other ERDAS IMAGINE accepted unit.

Download as ready to run.

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NDVI Change Detection model

Required inputs: Two date multispectral rasters of the same area, including band wavelengths: NIR and Visible Red.

This model creates two NDVI images for two date raster information then subtracts the NDVI values to produce a change layer. Then a threshold changes this change layer to a value of 1 for anything currently equal or larger than 0.6 and 0 for anything below 0.6. This model is particularly good for highlighting harvest times for fields, deforestation or vegetation phenology studies. It could also be used for urban studies but be aware that, currently, it will also extract newly bare fields as well as new urban areas.

Download as ready to run, or in an editable format.

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Raster Image Segmenter

Required inputs: A Raster Image

This models allows you to run a FLS segmentation operation on a raster image within the spatial model interface. Segmentation analysis is a useful function to run to find the zones of spectral similarity in an image. You can then use our Classification Segmentation model to combine this information with a classification thematic image.  This model comes ready with ports.

Download as ready to run.

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RGB Point Cloud Encoder

Required inputs: LiDAR Point Cloud (.las format) and a Raster Image of the same area

This model will encode the points in a Point cloud file with the corresponding colours from a raster image of the same location. This model comes ready with ports.

Download as ready to run.

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Performing a spatial radiometric transform using the Dodge Plus operator to enhance an image

Required inputs: A Raster Image

The Dodge Plus operator corrects the contrast and brightness in varying quantities over an image to enhance it for visual interpretation. It can compensate for gradual to moderate haze and light variations. You can use panchromatic or multispectral images (up to 4 bands) you will just need to remove or add band selectors for your required combination of bands. I have tested this for a Landsat 8 image.

Download as ready to run, or in an editable format.

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Two date Change Detection Visualisation

Required inputs:  Two 3 band raster images of the same area but captured on different dates

This model allows you to quickly view the change between two different images from seperate dates.  It combines the bands of the images so that you see one band of the old image within the new image.

Download as ready to run, or in an editable format.

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Auto-colour thematic Raster Images

Required inputs:  Thematic raster layer

This model allows you to add colours onto your classification or other thematic raster. This is helpful when you want to quickly standardise the colour of your classifications for easier comparison.

This model requires a bit more editing than usual as you need to select the colours you want in which row or by which attribute. A sample monochrome example has been included for you. Therefore a ready to run model is not available.

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Produce Four Output Sun-Shaded Datasets from a DEM

Required inputs: Digital Elevation Model

This model takes an elevation dataset and produces four output sun-shaded datasets. Sun elevation is set at 60 degrees and azimuth at 45, 135, 225 and 315 degrees.  Click here to view a screenshot.

Download as ready to run, or in an editable format.

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Landsat 8 QA Cloud Mask Model

Required inputs: A Landsat 8 tile including the QA data as a separate image file. 

This model takes the QA band provided with Landsat 8 and quickly masks out cloud from the multispectral image. This will result in the areas of cloud being recoded to a pixel value of 0.  You can find out more about the QA band information here: http://landsat.usgs.gov/L8QualityAssessmentBand.php

It is also worth taking a look at our YouTube video on how to easily import Landsat data into ERDAS IMAGINE: http://www.youtube.com/watch?v=VLsTkSBFYdw

Download as ready to run, or in an editable format.

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Define Urban Areas in OpenStreetMap

Required inputs: The Highways dataset from OpenStreetMap projectes to a meter grid such as UTM.

This spatial model defines urban areas in OpenStreetMap by looking at the density of residential streets, including service and secondary roads, then produces a raster urban output. The data needs to come from the Highways dataset from OpenStreetMap. This needs to be projected to a meter grid such as UTM for the model to work.

Download as ready to run, or in an editable format.

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Extract Slopes Over a Certain Steepness Part 2

Required inputs: DEM or DSM only, for NDVI version a multispectral image which includes the NIR and RED bands is also required.

This model allows the user to extract slopes of a certain steepness and aspect.  This version incorporates an NDVI which allows you to remove vegetation from the image or to restrict the image to show only vegetation. 

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Worldview Radiance Converter

Required inputs: A raw WorldView-2 Image.

You can use this Spatial model to calculate the DN values for a worldview-2 image for top of atmosphere spectral radiance.  However, be aware that this is not top of atmosphere reflectance (TOA) as the model does not correct for scene specific variations, including: earth-sun distance, solar zenith angle and topography.  You may want to check that the variables Abscal Factor and Effective Bandwidth have not changed for your scene.  Both values can be found in the metadata.

Download as ready to run, or in an editable format.

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Extract Slopes Over a Certain Steepness Part 1

Required inputs: DEM or DSM only.

This model allows the user to extract slopes of a certain steepness and aspect.  So, for example, it is currently set up to produce north facing slopes of over 60% steepness.  These thresholds can easily be edited to extract other slope ranges or face direction.

Download as ready to run, or in an editable format.

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Dividing a Scene into user Defined Slope Ranges

Inputs Required:  A terrain raster, generally a DTM or DEM.  

This model allows you to divide a scene into user defined slope ranges.  The model allows easy extraction of the flattest and steepest areas, which can help with planning as it is often cheaper or necessary to build of areas of low slope.

The terrain data is computed into degree slope levels, which ranges from 0 to 90 (horizontal to vertical). Then divided into 3 ranges of degree angles, the range boundaries can easily be edited in the criteria operator.  A terrain raster is necessary for this model, generally a DTM or DEM is best for this process.  

Download this model as ready to run, or in an editable format. 

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Convert Thermal DN Values to At-Satellite Temperature

Input datasets required: Landsat 8 Thermal (TIRS) two band data. Metadata (MTL) information.

This model converts thermal DN values to at-satellite brightness temperature, using the formula detailed here: http://landsat.usgs.gov/Landsat8_Using_Product.php

In all the examples that I've seen, the radiance add, radiance multiplier, K1 and K2 values have all been the same:

 


TIRS Band 10

TIRS Band 11

Radiance Multiplier

0.0003342

0.0003342

Radiance Add

0.1

0.1

K1

774.89

480.89

K2

1321.08

1201.14

However, you may want to verify this with your own data/metadata and adjust accordingly if they are different.

Download this model as ready to run, or in an editable format.

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Extract Areas of Flooding from Landsat 7 or 8 Images

Input datasets required:  Landsat 8 or 7 in .img format.  To use the cloud extraction, you will need a subset of the Landsat image over the cloud covered flood area.

This model was specially designed to pull out flooded areas in a Landsat 8 image of Somerset using two models to compensate for the cloud in the image. From this model a thematic image is produced, where flooded areas have a pixel value of 1. This image can then be vectorised in IMAGINE to produce flood polygons. This will run for any Landsat 8 or 7 image which contains the 9 usual bands, the Landsat images were taken from the USGS website then directly imported to .img by using the Import function in IMAGINE.

The main model utilises the Normalised Difference Water Index (NDWI), which uses bands 7 (SIR Shortwave Infrared) and 6 (Thermal infrared TIR). These bands are particularly good for water extraction. The accompanying model uses the near infrared (NIR) and red band to extract the flooding which is concealed by cloud. To extract flooding in the cloudy region a subset of this area is required.

Please note that to use this model on other scenes, the threshold values may need to be tweaked accordingly and the cloud model removed.

Download this model as ready to run, or in an editable format.

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Calculate the Top of the Atmosphere Reflectance on Landsat 8 Images

Input datasets required: Landsat 8 OLI. Metadara (MTL file) information.

This model converts DN values from Landsat 8 OLI data to Top of Atmosphere (TOA) Reflectance. It uses the formula detailed here: http://landsat.usgs.gov/Landsat8_Using_Product.php

In the examples used to create this model, Mp and Ap were constant across all bands. If this isn't the case with your data, you will either have to modify the model yourself or ask us to do it for you.

Download this model as ready to run, or in an editable format.

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Classification Segmentation

Input datasets required: A classification and image segmentation.

This model combines a spectral classification (supervised or unsupervised) with a segmented dataset. For each segment, it determines the maximum class within that segment (the class with the most number of pixels within the segment) and outputs that value to the segment.  This has the benefit of bringing a degree of spatial awareness to a classification. Aesthetically, it will tend to reduce the salt and pepper effect of a typical classification. However, depending upon the segmentation parameters one uses, it may also over generalise the classification. Use with caution!

Download this model as ready to run, or in an editable format.

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NDVI and Texture Model

Input dataset required: Data containing both NIR and visible RED wavelength bands.

This model combines an NDVI image with texture analysis. This can be used to extract areas that are flat or vegetated or non-vegetated, or it can be used to extract rough areas which are trees or building edges.  A thematic two colour raster image will be produced.  This model is especially good for grassland or field extraction.  We have provided the base model which has no simplification operations. This makes the model more amendable and it produces a detailed product for analysis. 

Download this model as ready to run, or in an editable format.

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NDVI Threshold Mask

This model utilises a multispectral image to produce a new raster layer showing only non-vegetated areas. This is created from an image that contains both the red and the near-infrared band. Firstly, the model produces a Normalised Difference Vegetation Index (NDVI). This NDVI image is then categorised into areas of vegetation and no vegetation; the no vegetation area is the default interested parameter.  Therefore, these pixels equate to 1 and vegetated areas have a zero value.
 
This model may need slight adjustment for different input scenes. Note that the threshold NDVI pixel value may need to be changed in the criteria function to a higher or lower pixel value.

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Tree Mask from Height Data

This model provides a new image which contains only two thematic types, consisting of tree features and other. This model can extract vegetation that is higher than three meters (the height is amendable if desired).  This model combines a multispectral image with a relative height layer. The model creates an NDVI layer which is then restricted to vegetation only, removing the possibility of extracting buildings. Then, using the relative height information, it subtracts areas of vegetation that are lower than 3 meters leaving only vegetation that is taller than 3 meters which is usually trees.
 
To obtain the necessary relative height layer, you can use our Relative Height Model, also provided in the library, to simply subtract a Digital Elevation Model (DEM) from a Digital Surface Model (DSM).

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Relative Height Model

This is a simple model to produce a relative height layer from a Digital Elevation Model (DEM) and a Digital Surface Model (DSM). A relative height layer generates the heights of features on the ground surface. Unlike a DSM, which, gives the cumulative height from a base level (often sea level), a DSM is inclusive of elevation changes.
 
This model is useful for workflows, where it is necessary to extract features of a certain height value or range.

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