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:
More details on the MSCD algorithm can be found here: http://www.mdpi.com/2072-4292/8/6/482/htm
Change Detection Threshold (CDT) algorithm makes a simple change detection product using the log ratio of the input radiometrically terrain corrected images: output = (Log10(input2/input1)).
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.
The InSAR GAMMA algorithms use the GAMMA software to create differential InSAR products.
The Sentinel-1 algorithm operates as follows:
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
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.
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.
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.
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.
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)
Small, D., 2011. Flattening gamma: Radiometric terrain correction for SAR imagery. IEEE Transactions of Geoscience and Remote Sensing, 49(8):3081-3093.
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/
A single RTC image can be useful in its own regard, however SAR can be exceptionally effective for time series analyses. Although the spatial resolution of SAR isn’t as robust as optical imagery, SAR’s ability to image the Earth in all weather, night and day, allows for a reliable series of images to be acquired for the same geographic region. The Sentinel-1 constellation of the European Space Agency’s Copernicus mission provides 6-day repeat pass coverage, which allows detection of rapidly changing features on earth’s surface. This means these “stacks” of images can be used to examine phenomena such as flooding, deforestation, disaster damage, or long term changes associated with natural or human processes.
To facilitate large-scale time-series analysis using SAR data, ASF has developed the RTC stacking tool. This tool can be used to resample, group, and cull a stack of co-registered RTC images produced in this case by ASF’s HyP3 cloud-based processing system. The RTC Stacking Tool produces a set of post-processed GIS ready GeoTIFF images as well as an animated GIF to visually note regions of change.
This process operates on HyP3 RTC products rather than level 1 SAR products. Therefore, this process isn't accessible via the One Time Processing or Subscription menus. Instead, it must be performed on a subscription from the subscription viewer by clicking on a subscription's name in the Current Subscriptions list.
InSAR is a valuable tool for geodesy and geophysics but factors such as temporal decorrelation and atmospheric error can distort the deformational readings and provide innacurate data. An InSAR time series can help mitigate these errors by proving many small baselines and filtering out potential atmospheric phase contributions. Atmospheric models can also be used to correct for the phase delays introduced in the troposphere. A time series animation can also be used to visualize the deformational evolution of a region.
This InSAR time series process uses the Generic InSAR Analysis Toolbox (GIAnT), a python suite that implements SBAS, N-SBAS, and MInTS algorithms as well as PyAPS to correct for tropospheric delays. Currently, only SBAS and N-SBAS are available for use with HyP3. Also available, the atmospheric correction option utilizes an ASF implementation of David Bekaert's Toolbox for Reducing Atmospheric InSAR Noise (TRAIN) as well as Caltech's Python Atmospheric Phase Screen Estimation (PyAPS) and the the MERRA-2 dataset. The output is a series of time ordered GeoTIFFs of the scene's cumulative displacement in mm as well as a gif showing the deformational evolution of the area, relative to the earliest aquisition. If the raw option is given, then the non-cumulative displacements are also output.
This process operates on HyP3 InSAR products rather than level 1 SAR products. Therefore, this process isn't accessible via the One Time Processing or Subscription menus. Instead, it must be performed on a subscription from the subscription viewer by clicking on a subscription's name in the current subscriptions list.
Additional information and the code used for this process can be found in the following repositories: HyP3 - GIANT and HyP3 - TRAIN.
- Bekaert, D.P.S., Walters, R.J., Wright, T.J., Hooper, A.J., and Parker, D.J. (2015c), Statistical comparison of InSAR tropospheric correction techniques, Remote Sensing of Environment, doi: 10.1016/j.rse.2015.08.035
- New Radar Interferometric Time Series Analysis Toolbox Released, P. S. Agram, R. Jolivet, B. Riel, Y. N. Lin, M. Simons, E. Hetland, M. P. Doin and C. Lassere, Eos Trans. AGU, 94, 69, 2013.
- P. Agram, R. Jolivet and M. Simons, Generic InSAR Analysis Toolbox (GIAnT) - User Guide (2012), http://earthdef.caltech.edu
- Jolivet, R., R. Grandin, C. Lasserre, M.-P. Doin and G. Peltzer (2011), Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data, Geophys. Res. Lett., 38, L17311, doi:10.1029/2011GL048757
- Jolivet, R., P. Agram and C. Liu, Python-based Atmospheric Phase Screen estimation - User Guide (2012), http://earthdef.caltech.edu
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.
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.