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Multi-Scale Relief Model - MSRM

An algorithm to make visible subtle raster changes at different scales

MSRM can be used to make visible near-invisible changes in Digital Surface Models, Digital Terrain Models, or RGB images. MSRM is different from other multi-scale microtopographic visualisation algorithms because it produces a single-band user-defined scale raster, which makes it ideal for combination with other rasters (as a background for visualisation, to apply other visualisation methods, or as part or a multiband raster used in methods such as ML/ML-based classification). It can be applied to different types of rasters, to highlight small value differences in neighbouring pixels. For example, when applied to each band of RGB images, it makes evident imperceptible changes in colour, improving local contrast.

Topography

image

Comparison between MSRM (G) and other micro-topogray visualisation tools

Images

Sent2 MSRM_RGB

Comparison between multitemporal cloud-free Harmonised Sentinel 2 image (2σ stretched) - top - and MSRM of the same image (2σ stretched) - down

The Multi-Scale Relief Model script was originally published in Earth Surf. Process. Landf. (2018), 43(6): 1361-1369. https://onlinelibrary.wiley.com/doi/10.1002/esp.4317
In this later implementation it has been updated with:

  • Parallel excution for each generated surface
  • Admits both projected and geographic (degrees) CRS
  • Visualisation of the resulting MSRM

Setting up the environment

Create and activate an env using the provided YAML file:

conda env create -f environment_MSRM.yml  # Creates the environemt and installs the pinned packages
conda activate MSRM  # Activates the environment
python -m ipykernel install --user --name MSRM --display-name "MSRM"  # Optional: registers your current Python environment as a selectable Jupyter kernel. No need to use if you are going to lauch Jupyter from MSRM environment (the YAML file installs Jupyter Lab/Notebook) <br>

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Multi-Scale Relief Model script originally published in Earth Surf. Process. Landf. (2018), 43(6): 1361-1369

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