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SUMMARY DOWNLOAD DESCRIPTION MSPA-PARAMETERS MSPA-EXAMPLES
Working on Guidos 2.0: 64bit support, change and fragmentation analysis tools, updated software components (GIS, GDAL, etc), release target 10/2012...
NEW Guidos 1.4 (3/2012): bugfix release (changelog) including new feature Conefor Inputs to generate Conefor Sensinode compliant input files from raster images, selecting 8/4-connectivity and Edge-to Edge or Centroid distance for the raster image objects:
Features of Guidos 1.3 (2/2010): processing of large images, quantifying the importance of nodes and links, network analysis.
SUMMARY:
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MSPA (Morphological Spatial Pattern Analysis) is a customized sequence of mathematical morphological operators targeted at the description of the geometry and connectivity of the image components. Based on geometric concepts only, this methodology can be applied to any scale and to any type of digital images in any application field. Here, the foreground area of a binary image is divided into the seven generic MSPA classes Core, Islet, Perforation, Edge, Loop, Bridge, and Branch (this demo illustrates the conceptual idea to derive the four basic classes Core, Islet, Perforation, Edge). This true segmentation process results in mutually exclusive classes which, when merged together, exactly correspond to the inital foreground area. Further details on MSPA can be found in Morphological Segmentation of Binary Patterns. Additional MSPA related publications are Mapping Spatial Patterns, Mapping Landscape Corridors, Mapping Functional Connectivity, Scale Analysis, Neutral Model Analysis (1) and (2), Green Infrastructure, and Detecting Key Connectors. A short MSPA summary is provided in the Guidos handout or you can download a summary presentation: odp (13.6 MB) or ppt (19.9 MB).
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DOWNLOAD:
The software GUIDOS (Graphical User Interface for the Description of image Objects and their Shapes) can be used to perform a Morphological Spatial Pattern Analysis (MSPA) on raster image data. MSPA conducts a segmentation of the image foreground data into mutual exclusive feature classes.
Download and follow the GUIDOS Installation Instructions. They contain important additional information to ensure a correct functioning of the application. GUIDOS is available in the following two download options (md5 checksums).
Please right-click on your version and choose 'Save Link as':
- GUIDOS: Install GUIDOS to your PC
(no administrator rights are required; if you have the choice, do yourself and your PC a favor and get the Linux or the Mac version where MSPA calculations are 40% faster compared to the MS-Windows version...).
- GUIDOS LiveDVD: Run GUIDOS and additional GIS software from a bootable LiveDVD without modifying your existing operating system. The DVD contains a complete Linux operating system which can also be installed to your computer.
GUIDOS includes the entire set of the GDAL libraries to process geospatial data and to export them as raster image overlays in Google Earth. Furthermore, you can visualize and pre- and post-process any raster or vector data with the included software FWTools and Quantum GIS.
To update to the latest Guidos 1.4:
- open the Guidos-directory (i.e. C:\Guidos) and rename/backup the file guidos.sav.
- Download and save the updated files guidos.sav (md5sum: a9ceb9e71315b5f439d92eb69ced953f) and GUIDOS_Manual.pdf into the Guidos-directory.
Please also send
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a copy of your publication in which this software has been used.
The graphical interface of GUIDOS
GUIDOS benefits from expertise in mathematical morphology developed by
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from the ISFEREA action of the IPSC-SES unit.
DESCRIPTION:
The MSPA segmentation is exemplified in the following image for a binary forest mask:
The key features of MSPA include:
- detection of connecting structures,
- distinction of external versus internal background (detection of holes),
- detection of deviations from a pre-defined thickness,
- user-defined analysis scale.
The generic nature of this purely geometric process allows applying MSPA at any scale, in any application field, and to any type of binary raster map (aerial photograph, land-cover map, satellite data, digital images, ...).
The following image describes the principle MSPA workflow, starting with:
- Preparation of the binary foreground/background mask: The expert (user) selects the appropriate input data providing the features of interest in an appropriate resolution in order to be detected. The input data is then pre-processed by the expert into a binary foreground/background map, where foreground corresponds to the target of interest and background is the complement to foreground. Examples for landcover binary masks could be a forest/nonforest mask, a wetland/non-wetland mask, a grassland/non-grassland mask, etc. Note that the definition of 'forest' for example is not unique and will depend on the application defined by the expert.
- MSPA analysis: The user can either use the default settings for the 4 MSPA parameters or modify them in order to fine-tune the edge-width of the resulting classes and/or detect connecting elements of a pre-defined width. More information on these 4 settings can be found in the GUIDOS manual or below.
- Interpretation of the MSPA segmentation: The generic naming scheme of the MSPA classes may need to be amended to match the nature of the input data. For example the class Perforation is the surrounding of a foreground hole. For a forest mask, such a foreground hole could be called a forest 'opening' while for a wetland mask such a hole is an 'island' (= a hole in a lake).
If the result is not satisfactory after the third step the user could run a subsequent analysis. For example, the MSPA-parameters could be amended to increase the edge-width of the resulting classes, or the user could perform a second analysis using a revised definition of foreground/background. The Guidos MSPA-segmentation in the box 2 below can be compared to a sophisticated camera. It can be used to take a simple snap-shot as well as for customized photography once you are familiar with the use and meaning of the additional camera (=MSPA parameter) settings. From the user's side, a stunning photo requires not only choosing the right scenery but also knowing and appropriately setting the camera settings to achieve this goal.
MSPA-PARAMETERS:
The following images show the effects of changing the 4 MSPA-parameters Foreground Connectivity, EdgeWidth, Transition, and Intext. More details on these 4 MSPA-parameters can be found in the MSPA-Guide within GUIDOS.
MSPA-parameter 1, Foreground Connectivity: the white circles show the difference when using 8- (left image) or 4-connectivity (right image) for Foreground Connectivity.
MSPA-parameter 2, EdgeWidth: increasing the EdgeWidth will increase the non-core area at the expense of the core-area and may change the pattern class (white circles). Changing the EdgeWidth has no impact on the total foreground coverage.
MSPA-parameter 3, Transition: transition can be set to either show connecting elements into core area (white circles, left image) or hide these transition pixels to maintain closed perimeters for the classes perforation and edge.
MSPA-parameter 4, Intext: Intext can be used to add a second layer of classes inside perforations to the 7 basic classes (see the pixel value examples). When Intext is on (1) a pixel offset of 100 is added to the feature classes in the internal areas of the foreground objects. More details on Intext including a detailed table with pixel values can be found in the MSPA-Guide within GUIDOS.  Parameter 4: Intext (811 <Intext>).
Left: Intext on (8111). Right: Intext off (8110)
MSPA EXAMPLES:
The following images illustrate MSPA examples for
MSPA detecting connecting structures, example 1: a water mask in Finland:
lakes (blue), rivers connecting more than one lake (red), rivers feeding one lake only (green), islands (yellow).
MSPA detecting connecting structures, example 2: Lost in a maze? No problem, let MSPA find the way out!
 
MSPA detecting external/internal background (holes): Example of deforestation in the Amazon, Rondonia, Brazil. Forest perforations (blue) and other pattern classes; exported as Google Earth image overlay using Guidos.
MSPA in manufacturing quality control: Example of simulated manufacturing defects on a circuit board:
Upper circle: introduction of a hole and a mis-aligned via. Bottom circle: opening the left ring structure changes the class loop into branch. Filling the right ring structure changes the class loop into core and bridge.
The comparison of a MSPA image against a MSPA-template can be used to detect incorrectly-sized, misplaced, insufficient, damaged, or missing components in manufacturing.
MSPA in medical images: Example of X-ray of Homer Simpson:
Brain showing impact of too much Duff but the connection to the brain is still active... Similarly, deviations from a pre-defined thickness can be detected, e.g., thinning or thickening of arteries.
MSPA for climate change analysis: spatial pattern of Sea-Ice showing openings (yellow) in a large ice-field (ENVISAT-Meris, 29/08/2006).
Quantifying connectivity: using Conefor Sensinode to quantify the relative importance of each MSPA detected core and connector.
Network analysis: showing indvidual network components and their properties.
Contact:
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