aaic user's guide - hexagon geospatial · aaic™ v.4.0 user’s guide 6 - applied analysis...

AAIC™ v.4.0 User’s Guide

Applied Analysis Inc. - 1

Module for ERDAS IMAGINE 2014

User’s Guide

Version 4.0

March 2014

Copyright © 2014 Applied Analysis Inc., All Rights Reserved.


2 - Applied Analysis Inc.

Applied Analysis Inc. 515 Groton Road, Suite 101 Westford, MA 01886 USA Phone: (978) 392-4500

Web: www.discoveraai.com E-mail: [email protected]

All information in this document, as well as the software to which it

pertains, is proprietary information of Applied Analysis Inc. and is subject to an Applied Analysis Inc. license and non-disclosure agreement.

Neither the software nor the documentation may be reproduced in any manner without the prior written permission of Applied Analysis Inc.

TRADEMARKS ERDAS IMAGINE is a registered trademark of the Intergraph Corporation. This product includes GDAL (open source) software developed by The XFree86 Project, Inc (http://www.xfree86.org/) and its contributors. Its use is subject to the following conditions: Copyright (c) 2000, Frank Warmerdam. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Other brands and product names are trademarks of their respective companies.



Table of Contents 1.1 Introduction to AAIC™ Image Calibration Software ...................................................................... 5

1.1.1 Background ........................................................................................................................... 5 2.1 Algorithm Description .................................................................................................................... 5

2.1.1 Performance Characteristics ................................................................................................. 7 2.1.2 Important Operational Notes ................................................................................................. 8

3.1 AAIC Software Operational Steps ................................................................................................. 8 3.1.1 Getting Started with the Software ......................................................................................... 8 3.1.2 Guidelines for Data Entry ...................................................................................................... 9 3.1.3 Step-by-Step Guide to Using the AAIC Software .................................................................. 9

3.1.3.1 Starting a Session ............................................................................................................. 9 3.1.3.2 Processing Setup ............................................................................................................ 11 3.1.3.3 AAIC Processing ............................................................................................................. 22 3.1.3.4 AAIC Results ................................................................................................................... 24 3.1.3.5 Expected Results from AAIC ........................................................................................... 25

Table of Figures Figure 1. AAIC Processing Flow ................................................................................................................... 6 Figure 2. Selecting the Autonomous Atmospheric Correction Tool from the ERDAS IMAGINE tool bar ... 10 Figure 3. AAIC Main menu window ............................................................................................................. 10 Figure 4. Image file selection ...................................................................................................................... 11 Figure 5. Sensor selection .......................................................................................................................... 12 Figure 6. Sensor type selected ................................................................................................................... 13 Figure 7. Selecting new output location ...................................................................................................... 14 Figure 8. Selecting output Material ID file ................................................................................................... 15 Figure 9. Specifying output Material ID file ................................................................................................. 16 Figure 10. Ready to launch processing ...................................................................................................... 17 Figure 11. Selecting the Other sensor option ............................................................................................. 18 Figure 12. Other sensor option selected ..................................................................................................... 19 Figure 13. Selecting the band center and widths file .................................................................................. 20 Figure 14. Band center and widths file selected ......................................................................................... 21 Figure 15. Ready to run calibration using Other sensor type ..................................................................... 22 Figure 16. Pre-processing status window ................................................................................................... 23 Figure 17. Reflectance image creation status window ............................................................................... 23 Figure 18. IMAGINE Viewer with the results of AAIC processing ............................................................... 24 Figure 19. AAIC reflectance image swipe ................................................................................................... 25 Figure 20. Spectral plots for pixels covering vegetation ............................................................................. 26 Figure 21. Example 1 - Reflectance image on the left and Material ID layer on the right .......................... 27 Figure 22. Example 2 - Reflectance image on the left and Material ID layer on the right .......................... 28 Figure 23. Example 3 - Reflectance image on the left and Material ID layer on the right .......................... 28 Figure 24. Example 4 - Poor calibration results with reflectance image on the left and Material ID layer on

the right ............................................................................................................................................... 29 Figure 25. Example 5 - Poor calibration results with reflectance image on the left and Material ID layer on

the right ............................................................................................................................................... 29



-Notes-



1.1 Introduction to AAIC™ Image Calibration Software

1.1.1 Background

AAI’s Image Calibrator (AAIC™) is a sophisticated tool for correcting imagery to material reflectance. This utility is provided as an integrated module for running under Intergraph’s ERDAS IMAGINE 2014 image processing software. The tool is designed to correct imagery for atmospheric conditions, sensor viewing angle, and sensor effects, all of which can impact the interpretability of remotely sensed imagery. These contributions, together with illumination differences, distort the recorded material radiance spectra and limit the ability of most spectral processing algorithms to properly and consistently identify materials of interest. By calibrating to ground reflectance units, spectral image exploitation algorithms can perform with higher degrees of sensitivity and accuracy, and provide more uniform and meaningful results across images.

Alternative calibration tools require the user to identify bright and dark pixels associated with known reflectance materials or deployed radiometric calibration panels, and/or specific weather and atmospheric conditions together with relative solar position to feed most model-based approaches to image calibration. AAIC™ provides the user with a fully automated processing approach, reducing the need for user controls and interaction. It is easy to use and provides the user with a calibration confidence figure of merit that indicates the accuracy achieved with the correction. In addition, an option to generate a Material Id layer provides the user with another measure of calibration performance.

2.1 Algorithm Description

AAIC™ image calibration software represents a new approach to spectral image calibration. The process is completely scene-data driven, which does not require external data or models. The innovative approach used within AAIC™ effectively suppresses the apparent spectral distortion introduced by atmospheric and sensor effects and calibrates an image to material reflectance. This calibration enables reliable use of spectral signatures calibrated in units of reflectance for improved information extraction from remotely sensed imagery. Like other environmental correction methods, AAIC™ derives band-wise offsets (ACF(n)) and scale factors (SCF(n)) that effectively characterize the atmospheric radiance, atmospheric attenuation, and sensor transfer functions. AAIC™ incorporates a set of “universal” probe signatures derived from multiple calibrated reference scenes. The process uses these signatures to detect a suite of materials in the scene of interest. The reflectance spectra of the probe signature materials are known. Because a probe signature is detecting the same material in the scene of interest at a subpixel level, the reflectance spectrum of its detection in the scene-of-interest can be determined. The band-wise environmental correction scale factors and offsets for the scene of interest are the only unknowns. Through an adaptive process, the reflectance spectrum of each



probe signature is associated with the reflectance spectrum of its detections in the scene of interest, thereby allowing the correction factors for the scene of interest to be determined. Figure 1 provides a summary flowchart of the AAIC™ process.

Figure 1. AAIC Processing Flow

AAIC™ is a completely automated process. It is also an iterative process, starting with an initial stage estimate of ACF(n) and SCF(n) based on simple normalization. ACF(n) and SCF(n) are then refined using pre-computed ground cover spectra from the image, retrieved using a specially adapted unsupervised classification process. This intermediate stage estimate involves identifying best-fit matches between the ground cover spectra from the reference scenes and those from the scene of interest. The reflectance spectra of the best-matched spectra are assumed to be approximately the same, enabling an intermediate stage ACF(n) and SCF(n) to be derived for the scene of interest.

Multispectral or Hyperspectral Image

Pre-Processing (Creating Background Spectra)

Probe Signature

Processing (Compare Backgrounds with

Probe Signatures, Find Best Fit)

Review AAIC Results

Refining ACF and SCF

(Final CORENVs derived)

Applying ACF and SCF

(Reflectance Image Creation)



The final stage of automated iteration enables the needed level of accuracy to be achieved. This step of the processing uses a set of universal probe signatures derived from reference scenes. Probe signatures are built into the software and the user does not have to specify them. The software automatically compensates for sensor differences or probe signature variations and, therefore, does not require the user to select bright and dark materials within the scene or know which materials are present in the scene or their location. By using specific land cover probe signatures, the process automatically avoids clouds, high elevation shadows, and other artifacts, thereby eliminating the need for operator interaction. The user only needs to specify the image sensor attributes (spectral band centers and widths) and AAIC™ runs fully autonomously. AAIC™ accommodates a wide variety of sensor types allowing for calibration of imagery from just about any multispectral or hyperspectral sensor. During the course of comparing calibrated image samples to probe signature spectra, the image calibration process keeps track of which probe signatures perform best for these samples and computes the band-wise ratio of the calibrated spectrum to the probe spectrum. These ratios can be combined into a single figure of merit and expressed as a percentage – the confidence value. Images with high confidence values contain good matches to the probe signatures and should be calibrated accurately. Thus, the reported confidence value is a measure of how well the process performed.

2.1.1 Performance Characteristics

The accuracy of AAIC™ products was tested using a suite of imagery containing deployed panels and other ground materials with independently calibrated field measured reflectance spectra. The imagery test suite included 18 hyperspectral (HYDICE and AVIRIS) and multispectral (QuickBird, IKONOS, and Landsat TM) images covering a wide range of representative land cover conditions. The accuracy was measured in terms of mean (average across spectral bands) absolute reflectance difference between the image-derived and field measured spectra for each panel or ground material. Each image contained 4-7 panels spanning from low reflectance (0.02-0.04) to high reflectance (0.60-0.64), and the accuracy for the set of panels was averaged to produce a mean accuracy value for the image. The mean of the reflectance differences from the field-measured data for all images was 0.0269, while the standard deviation was 0.0112. This means that pixel reflectance of most AAIC™ products should be within 0.05 (mean + 2 standard deviations) of the actual ground reflectance values. The Calibration Confidence metric represents the probability that reflectance accuracy meets or exceeds this absolute mean difference value. Processing times vary with image size and processing engine, but they average 18 min per 100 Mb image size for a 3GHz/2core processor.

In addition to the internal AAIC™ testing that was done at AAI, independent evaluation was performed by 3 of the top providers of image processing software. Their findings generally confirmed the internal findings. One situation that should be noted is that images with lower-than-acceptable land cover may have lower-than-acceptable reflectance accuracy. An image having <



10% land cover, for example, may cause reported pixel reflectance spectra to have lower-than-expected reflectance accuracies, while reporting a Calibration Confidence > 85%.

2.1.2 Important Operational Notes

The resulting output files that can be created with AAIC™ include a Material Identification image that indicates, using standard material spectra, how well the image calibration performed. This is a reduced resolution image which will be tied, in terms of geographic position, to the upper left corner of the input image. It will not properly overlay the input image since the spatial resolution has been degraded, however, if gives the user an idea of how well the various land covers in the original image get identified as the appropriate material in the Material Identification image. An expanded discussion with examples, that shows the user how to interpret the Material Identification image layer, is included in section 3.1.3.5 Expected Results from AAIC™. With regards to processing hyperspectral imagery with AAIC™, the user should be aware of three items. The first is that the inclusion of image bands that fall in the range of the water absorption wavelengths (1350nm – 1435nm, and 1815nm – 2000nm) will cause poor AAIC™ performance. Spectral bands that fall in these ranges should be removed prior to processing. It is recommended that the user visually review the spectral bands that border this range to make a final decision about whether to exclude a band or not. If the image band shows mostly noise, with little distinction of features in the image, then the band should be removed. Second, it is important to maintain the spectral band order in the image such that the wavelength is ascending in value as the image bands increment upward. And third, excessively overlapping spectral bands also may result in poor AAIC™ performance, and should be removed. This is true for AVIRIS, where there is a set of bands that is offset from the entire set such that spectral coverage is repeated. Leaving the image in this state will result in poor AAIC™ performance since image bands will not maintain the wavelength ascending order criteria.

3.1 AAIC Software Operational Steps

This section describes how to get starting using the AAIC™ software. It reviews the guidelines for data entry requirements of AAIC™, a step-by-step review of how to use the AAIC™ software, as well as providing detailed instructions on reviewing and interpreting AAIC™ results.

3.1.1 Getting Started with the Software

The AAIC™ software is integrated with Intergraph’s ERDAS IMAGINE to take advantage of its image handling tools. The most commonly used tools with AAIC™ are:

Viewer Window for Image Display

Image Manipulation Tools

Arrange Layers control



Zoom

Swipe

Pan (for moving around the image)

For a detailed discussion of these functions, the user should refer to the documentation that is supplied with the ERDAS IMAGINE software.

3.1.2 Guidelines for Data Entry

Data necessary to run AAIC™ are entered via dialog boxes. Four important data entry guidelines must be followed when using the AAIC™ software.

1) This version of the AAIC™ software only accepts images in the NITF, GeoTIFF, and Erdas IMAGINE *.img image format. To work with other data formats, import the image into IMAGINE *.img image format and process the image in this format. The reflectance image will be output in the Erdas IMAGINE *.img format if the input image is in NITF format. An input GeoTIFF will result in an output reflectance image in GeoTIFF format. 2) The AAIC™ software cannot accept images with “.” in their base file names or in their file paths. For example, file.name.img cannot be used. Please make sure all input images meet this requirement. 3) The AAIC™ software will not accept input images with a space in their base file names of in their file paths. The user must rename the image or move it to a different location. 4) AAIC™ only processes multispectral and hyperspectral imagery; it does not process panchromatic imagery (single band).

3.1.3 Step-by-Step Guide to Using the AAIC Software

This section explains in detail how to run AAIC™ to perform image calibration. It discusses the results of the calibration process and provides tips for additional uses of these results.

3.1.3.1 Starting a Session

To begin using the AAIC™ software, do the following: 1) Start the ERDAS IMAGINE software and begin with either the default

workspace or load an existing one. 2) If the image to be processed is currently loaded in the IMAGINE Viewer,

the AAIC™ Wizard Main Menu input fields will automatically be populated with the image file name and outputs when the program is started.



3) From the main menu bar, click the Raster tab > Radiometric > Autonomous Atmospheric Correction.

Figure 2. Selecting the Autonomous Atmospheric Correction Tool from the ERDAS IMAGINE tool bar

The AAIC Wizard Main Menu appears.

Figure 3. AAIC Main menu window



3.1.3.2 Processing Setup

1) To begin a new AAIC™ processing run, enter an image file name by navigating to the file and selecting it.

2) If the image file name contains characters that are related to the sensor

type (i.e. OV05 = GeoEye), the sensor type will be auto-selected and the user should confirm that the sensor type is correct before proceeding.

Figure 4. Image file selection

3) If the user has the image (to process) displayed in the Viewer, AAIC™ will automatically fill-in the input image file name.

4) The software will pair down the sensor selection list based on the number

of bands in the image (i.e. 4 band image list excludes Landsat TM, since images from this sensor typically contain at least 6 bands, not including the thermal band)



Figure 5. Sensor selection

5) The user should confirm that the correct sensor type is selected. Incorrect

sensor information will result in poor quality calibration results. The current set of sensors restricts the use of AAIC™ to data from the IKONOS, GEOEYE1, QuickBird2, Landsat MSS/TM, WorldView2, PLEIADES and SPOT satellites. Additional sensors can be added upon request.

Important note: If the user wants to process an image from a sensor not listed, there is an option to choose “Other” from the dropdown list and provide a band center and width file. Details on using a *.bcw file can be found by contacting AAI Customer support (see the end of this document). When using SPOT or PLEIADES imagery, be sure that spectral bands are properly ordered in the input image in terms of wavelength ascending order



(i.e. band 1 = blue band, band 2 = green band, …, band 4 = NIR band). GeoTIFF and JPEG2000 images for SPOT and PLEIADES typically have their bands ordered such that band 1 = red, band 2 = green, band 3 = blue, and band 4 = NIR. In their native format, the user would assign band 1 to red, band 2 to green, and band 3 to blue to get a true color display of the image.

6) Once the sensor type has been selected, the user will now see that the Reflectance Image File input window will be auto-populated with the root image file name and a “*_reflec” added to the file name prefix. The output file format is always IMAGINE img (except for GeoTIFF input, where the reflectance image is output in GeoTIFF format), even if the input image is in NITF format.

Figure 6. Sensor type selected

7) The user can redirect the output by clicking on the file browse button and navigating to a new folder destination.



Figure 7. Selecting new output location

8) In addition to the reflectance image, the user can specify an output Material Id layer which categorizes land cover types based on their reflectance properties. This layer is helpful to review for quality assurance purposes. It provides the user with an idea of the degree of success that was achieved with the calibration process.



Figure 8. Selecting output Material ID file

9) This Material Id layer is a reduced resolution single band image which provides an indication of image calibration quality. For example, if a large water body gets identified as soil or vegetation, then that is an indication of poor calibration quality and the user should consider using another image. Examples are provided in section 3.1.3.5 Expected Results from AAIC™. Reporting these types of results to Applied Analysis will help in making the software more robust for future software releases. See the end of this document for contact information.



Figure 9. Specifying output Material ID file

10) With all the processing options and output now selected, the user is ready to launch the calibration process by clicking on the OK button.



Figure 10. Ready to launch processing

11) If the sensor type is not provided in the drop down list, the calibration process can be run by providing an external file which contains the sensor band centers and widths.

a. After the user has provided the input image file name, clicking on

the Other option, found at the bottom of the drop down, will prompt the user to provide a band center and widths file.



Figure 11. Selecting the Other sensor option

b. The user will now see a new input box in the Wizard window that is used for specifying the band center and width file



Figure 12. Other sensor option selected

c. Navigate to and choose the input band center and widths file.



Figure 13. Selecting the band center and widths file

d. If the user doesn’t already have a *.bcw file, they will need to supply Applied Analysis Inc. with specific details about the sensor band passes. Specify the center wavelength and width (in the same units as the center wavelength - nanometers) for each of the spectral bands that comprise the sensor. For example, a 4 band IKONOS image will have 4 rows of entries, the first column containing the center wavelength and a second column, which contains the full band width.



Figure 14. Band center and widths file selected

12) The Batch button allows the user to accumulate processing runs into a single batch run for immediate hands-off processing or scheduling for launch at a later time. It utilizes the batch capability built into IMAGINE (see Batch button highlighted in Figure 15).



Figure 15. Ready to run calibration using Other sensor type

3.1.3.3 AAIC Processing

In the AAIC Wizard window, once the OK button has been clicked, the calibration process will begin. Pre-Processing is performed as an initial step. Pre-processing will automatically classify the entire image area into a maximum of 64 background classes. Depending on the scene diversity, it is not unusual to have fewer than 64 background classes. The output from this pre-processing step is an *.aasap file, which spectrally characterizes the content of the image. It can be re-used to process the image again if necessary. The larger the input image, the longer it will take to run pre-processing as the program needs to sample every pixel in the image. A 1 GB image may take as long as an hour or more to process, depending on the processing speed of the workstation.



1) As the AAIC™ process runs, various status windows will appear in the IMAGINE Session Log window.

Figure 16. Pre-processing status window

Once the pre-processing step is complete, the AAIC™ process moves on to artifact removal and a status bar indicates the progress of this step (Figure 16).

After artifact removal has completed, the probe signature processing begins. The status window shown in Figure 17 provides the user with an indication of the progress of this step in the overall processing. The larger the image, the longer this process will take. Hyperspectral images, for example, will require more processing time due to the greater number of spectral bands, even if the image dimensions are relatively small.

Figure 17. Reflectance image creation status window

2) When AAIC™ has reached completion, the user will notice that the

session log will report a completed status. For a successful run, the user should see the message that aicwrapper_aaic.exe exited normally. If errors were encountered during the run, the user may notice an exiting status other than successful. Scrolling back through the processing log will reveal at what point in the processing the error occurred, and also the probable cause.



3) After the image has been processed, the user can load the reflectance

image into the IMAGINE Viewer to compare it with the un-calibrated input image.

Figure 18. IMAGINE Viewer with the results of AAIC processing

4) Figure 19 shows the image before and after calibration.

3.1.3.4 AAIC Results

The AAIC™ software generates a number of output files which represent actual results and also report how well the image calibration worked. The pre-processing step generates an *.aasap file which is used by AAIC™ later in the process. There is no information in the file that the user needs to review, and the majority of the contents are in a binary format with the exception of the header section of the file.

At the completion of the AAIC™ processing, a *.corenv file is created, which contains the environmental correction factors that were derived for the image. These factors were applied to the input image to create a reflectance image. The correction factors also appear in the report file. The user typically does not need to review these values by opening the *.corenv file. These values are also stored in the header of the input file and can be reviewed by using the IMAGINE Metadata tool. An additional tab will appear that lists the correction factors.



The output reflectance image is the most important of the output files from the AAIC™ process. This image contains pixels that have been converted to values of material reflectance during the AAIC™ process. Images in this format, for example, can be used with spectral matching algorithms to allow for the identification of materials in the image using lab derived material spectra. The image is output as a signed 16 bit image but can be scaled back to 8 bit depth if desired, although some spectral detail may be lost by doing so.

3.1.3.5 Expected Results from AAIC

The user can expect AAIC™ to provide results that will improve the utility of the image being processed. Visually, most images will show an improvement in overall contrast due to a reduction of the scattering of electromagnetic energy in the visible blue region of the spectrum (Figure 19). Review of the digital data, in the form of spectral plots and histograms of statistical data, can also be used to get an indication of the impact of the AAIC™ process has on converting imagery to material reflectance (Figure 20).

Uncalibrated image is shown on the left and the calibrated image is on the right.

Figure 19. AAIC reflectance image swipe

After Calibration Before Calibration



Figure 20. Spectral plots for pixels covering vegetation

The top plot shows the spectral response before AAIC and the bottom plot shows the response after calibration. Note the suppression of values in the blue band (band 1) since this band is most influenced by haze in the atmosphere.

A few items to note that may indicate a problem with the input image and/or the AAIC processing:

1) If the reported confidence falls below 70%, it is likely that AAIC™ did a

poor job with the matching of probe signatures and the calibration may be in question. Confidence levels below 70% will not produce good results using follow-on processing algorithms (i.e. spectral matching), and the user should consider using another image if available. Confidence should be in the range of 85% - 95% to assure a successful run.

2) The correction factors should not be equal to 0 or 1 in all or several image bands. This is an indication of a problem with the processing and should be reported to Applied Analysis Inc. promptly.



3) Poor quality Material ID image: Visual comparison of known land cover

types (image-interpreted) with the class that they were assigned to in the Material Id layer gives the user an idea of the calibration quality. If, for example, a large water body gets identified as soil or vegetation, that is an indication of a poor calibration and the user should consider using another image. For the demo example presented in this document, the calibration quality appears good since the Material Id layer looks reasonable (see Figure 21).

Figure 21. Example 1 - Reflectance image on the left and Material ID layer on the right

Images with a high percentage of clouds and cloud shadows will not perform well with AAIC™ and the user should not expect removal of clouds and thick haze that exists in the input image. Also, the current version of AAIC™ may not generate accurately calibrated images in areas of high snow/ice cover.

The following figures show calibration results for two other images along with their associated Material Id layers.





The following figures show the results when a poor calibration is achieved. The reflectance image and associated Material Id layer are shown. In Figure 24, the water in the image is identified as senescent vegetation, which is a definitive indicator that calibration performance is poor. The user should attempt to find another image covering the area of interest and not use the calibrated image for any further processing.



Figure 24. Example 4 - Poor calibration results with reflectance image on the left and Material ID layer on the right

In Figure 25, the calibration is questionable since there is a large area in the open water that is being identified as soil. In addition, the land area does not show much detail, which is indicative of an underperforming calibration. If possible, the user should attempt to find a better image since any information derived from this image will be suspect.

Figure 25. Example 5 - Poor calibration results with reflectance image on the left and Material ID layer on the right

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