About HyP3

The Hybrid Pluggable Processing Pipeline, or HyP3 (pronounced "hype"), is an effort to provide custom on-demand SAR processing for users.

The system provides a limited amount of processing for a user per month. Currently, the HyP3 system is in beta, so access is limited. If you are interested in trying HyP3, please contact ASF at uso@asf.alaska.edu. We would be happy to provide access in exchange for feedback on the products and the interface.

Currently we provide a number of different implementations of Interferometric SAR (InSAR), Radiometric Terrain Correction (RTC), and change detection algorithms, as detailed below.

Citation information for each algorithm is below. If you wish to cite the HyP3 system itself, that is below the algorithm information.


Processes

About:

The Multi-Scale Change Detection (MSCD) algorithm was designed to enable consistently high-performance change detection from SAR images acquired over a wide range of hazardous situations. To provide consistent performance, the approach utilizes fully adaptive unsupervised techniques to identify optimal change detection thresholds for each evaluated data set. It aims to obtain high accuracy change detection maps in both heterogeneous and homogeneous regions by using information at different resolution levels. The MSCD methodology has four key features:

  1. Improved classification performance by combining Expectation-Maximization algorithms with mathematical morphology
  2. High accuracy in preserving the boundary of changed regions by implementing a low computational complexity method that uses measurement level fusion techniques
  3. Robustness against noise due to a combination of non-local filtering and two-dimensional discrete stationary wavelet transforms (2D-SWT)
  4. Applicability to a wide range of change detection problems due to an efficient and fully adaptive change detection workflow

More details on the MSCD algorithm can be found here: http://www.mdpi.com/2072-4292/8/6/482/htm


Version History:
09/27/2017
- Now include the cross-pol product as well (if available)
09/11/2017
- Support for PALSAR
08/03/2017
- Better Handling of boundry regions especially when clipping to ROI
- Fixed always clipping to ROI even when user doesn't ask for it
About:

The Simple Algorithm for Change Detection (SACD) algorithm makes a simple change detection product using the log ratio of the input radiometrically terrain corrected images: output = log10(input1/input2). The resulting image is then thresholded using defaults of -0.25 and 0.25 respectively, e.g. values less than -0.25 are considered to be negative change and are given threshold values of 1, values greater than 0.25 are considered to be positive change and are given threshold values of 3. Any pixels between -0.25 and 0.25 are considered to be stable and are given threshold values of 2. Background pixels are set to zero.


Version History:
09/11/2017
- Support for PALSAR
08/03/2017
- Support Cropping to ROI, use black backgrounds
About:

The glacier tracking software is able to estimate the surface velocities of glaciers by tracking the surface features and the speckle pattern in a pair of co-registered SAR images acquired on different dates. The software utilizes offset intensity tracking by applying a cross-correlation optimization procedure. This is performed by co-registering the image pair to sub-pixel precision, dividing one image into rectangular windows, and then determining the offsets to the corresponding patches in the other image from the maxima of 2-D correlation functions. The analysis is conducted primarily through GAMMA Remote Sensing Software, and the velocity map graphics are produced through the R programming language.

This specific algorithm was designed by the University of Erlangen-Nuremberg (FAU) and has been previously used to quantify spatial and temporal variabilities in the velocities of surging glaciers such as those in the Karakoram region. The Alaska Satellite Facility (ASF) at the University of Alaska Fairbanks (UAF) has modified the software to make it more robust and also capable of migration into the Amazon Cloud.


Citation:

Rankl, M., Kienholz, C., and Braun, M.: Glacier changes in the Karakoram region mapped by multimission satellite imagery, The Cryosphere, 8, 977-989, https://doi.org/10.5194/tc-8-977-2014, 2014.

About:

The InSAR GAMMA algorithms use the GAMMA software to create differential InSAR products.

The Sentinel-1 algorithm operates as follows:

  1. Given two input SLC images, determine which bursts overlap
  2. Create a Digital Elevation Model (DEM) file that covers the overlap bursts
  3. Create a look up table between DEM and SAR imagery
  4. Prepare the simulated phase using the DEM height for differential interferogram generation and to generate a look-up-table between the master and slave images for co-registration with the DEM
    1. Remove the flat earth phase
    2. Remove the topographic phase
  5. Repeat 3 times:
    1. Remap the slave image using previous matching result
    2. Match the slave image with the master image to refine offsets
  6. Further refine the offset between master and slave image using Enhanced Spectral Diversity
    1. Resample the slave image to match master
    2. Create final interferogram
  7. Unwrap the phase using the Minimum Cost Flow algorithm
  8. Geocode results and create output products
    The ALOS algorithm operates as follows:
    1. Given two input L1.0 data files, create the master and slave SLC images
    2. Create the initial interferogram by matching the master and slave images
    3. Remove the flat earth phase
    4. Create the simulated SAR image and match with master image to get exact mapping from DEM to SAR space
    5. Remove the phase due to topography
    6. Unwrap the phase using the Minimum Cost Flow algorithm
    7. Geocode the results and create output products

GAMMA Software: https://www.gamma-rs.ch/no_cache/software.html

General list of GAMMA Software References: https://www.gamma-rs.ch/uploads/media/GAMMA_Software_references.pdf

Specifics of Sentinel-1 Support in Gamma: https://www.gamma-rs.ch/uploads/media/2015-3_S1_Support.pdf

Phase unwrapping: https://www.gamma-rs.ch/uploads/media/2002-4_PhaseUnwrapping.pdf


Version History:
11/07/2018
- Use the wrapped phase image as the preview image
10/31/2018
- Added water masking option
09/27/2017
- Allow selection of 10x2 looks (default is 20x4)
09/08/2017
- Fix problem with burst overlaps in swaths 2 and 3
08/17/2017
- Support for PALSAR added
08/03/2017
- Standardized Naming
Citation:

Rosen, Paul A., Eric Gurrola, Gian Franco Sacco, and Howard Zebker. "The InSAR scientific computing environment." In Synthetic Aperture Radar, 2012. EUSAR. 9th European Conference on, pp. 730-733. VDE, 2012.


Version History:

The Notify Only processing type is a feature which allows you to be notified when new data arrives in an area you're interested in, but doesn't actually do any processing. You'll get an email letting you know that new data has been added to the ASF catalog; the HyP3 system won't do any processing.

The email will have a link to the ASF datapool where you can download the product.

About:

The RGB decomposition enhances dual-pol data for visual interpretation. It decomposes the co- and cross-pol signal into simple bounce (polarized) with some volume scattering, volume (depolarized) scattering, and simple bounce with very low volume scattering. These are assigned to the red, green and blue color channels respectively. In the case where the volume to simple scattering ratio is larger than expected for typical vegetation, such as in glaciated areas or some forest types, a teal color (green + blue) is used. The RGB decomposition takes RTC imagery as input. The output values are scaled between 0 and 255 and saved as a byte image.


Version History:
08/17/2017
- Reimplemented back-end to use amplitude RTC products
08/14/2017
- Rolled back 8/8 change due to it breaking RGB generation
About:

The RGB color difference is used to characterize the changes in backscatter behavior between the acquisitions of two dual-pol images, i.e. showing the backscatter change due to some natural hazard. It is based on the RGB decompositions of the individual images. While the red and green channels are taken straight from the post-event RGB decomposition, the blue channel is a scaled version of the difference of the green channels, representing the change in volumetric scattering. This product can be useful in the case of an event that reduces the volume scattering signature, such as strong wind, hail, or tornadoes. The output values are scaled between 0 and 255 and saved as a byte image.


Version History:
09/27/2017
- Support specifying a different threshold for the blue channel
08/17/2017
- Reimplemented back-end to use amplitude RTC products
08/14/2017
- Rolled back 8/8 change due to it breaking RGB generation
About:

The GAMMA radiometric terrain correction algorithm uses the GAMMA software to create GIS-ready, geometrically and radiometrically corrected SAR imagery products. The procedure uses a Digital Elevation Model (DEM) covering the SAR imagery to create a simulated radar image. This simulated image is then matched with the real SAR image to create a precise mapping from SAR space into DEM space (in this case, UTM projection). This mapping is then used to move all SAR pixels into a geocoded product. After remapping, a radiometric correction is applied using the pixel-area integration approach (Small 2011). Finally, the resulting RTC image along with ancillary products are converted to geotiffs, jpgs, and kmzs for ease of use to the end user.

GAMMA Software website: https://www.gamma-rs.ch

Algortihm Theoretical Basis Document for ALOS: https://media.asf.alaska.edu/uploads/RTC/rtc_atbd_v1.2_final.pdf
(Note: this covers ALOS, but the same science principles apply for Sentinel)


Citation:

Small, D., 2011. Flattening gamma: Radiometric terrain correction for SAR imagery. IEEE Transactions of Geoscience and Remote Sensing, 49(8):3081-3093.


Version History:
11/07/2018 - v1.2
- Improved matching in the 10m products
11/07/2018
- Version numbers correctly populated in metadata
11/01/2018 - v1.1
- Fixed multilooking in 10-meter products
10/31/2018
- Fixed GAMMA version number in Readme
08/08/2017
- Fixed precision orbit fetching
About:

Sentinel-1 Toolbox Terrain Correction will geocode the input image by correcting SAR geometric distortions using a Digital Elevation Model (DEM) and producing a map projected product. Geocoding converts an image from ground range geometry into a map coordinate system. Terrain geocoding involves using a DEM to correct for inherent SAR geometry effects such as foreshortening, layover and shadow. In addition, terrain flattening is applied, correcting for radiometric distortions resulting from varying terrain incidence angles. Prior to correction, precise orbits files are used, thermal noise is removed, the imagery is calibrated, and a speckle filter is applied.

S1TBX website: http://step.esa.int/main/toolboxes/sentinel-1-toolbox/


Version History:
08/10/2017
- Upgraded to S1TBX v5
- Removed the LIA file
11/30/2017
- Allow resolution selection
- Use hyp3 library for emails, etc

How to Cite HyP3 Data Products:

Hogenson, K., Arko, S.A., Buechler, B., Hogenson, R., Herrmann, J. and Geiger, A., 2016. Hybrid Pluggable Processing Pipeline (HyP3): A cloud-based infrastructure for generic processing of SAR data. Abstract [IN21B-1740] presented at 2016 AGU Fall Meeting, San Francisco, CA, 12-16 December.