PLUS Software Development IP

Software Development Application Development Object-based Image Analysis

Steps

• Implement image import and band selection

  • Image import

• Implement index calculation

  • User option to select image bands/layers for specific bands
  • Execute button to calcalute index

• Indices

  • Normalized Difference Vegetattion Index (NDVI) (nir - red)/(nir + red)
    The value of NDVI ranges between -1.0 to +1.0. The NDVI values near +1.0 (usually (0.6 to 0.9) indicate the dense vegetations; the negative values -1.0 to near 0 represents clouds, water, and snow; values near zero to 0.1 means barren rock, sand, or snow; moderate NDVI values +0.2 to +0.5 indicate sparse vegetations such as shrubs and grasslands or senescing crops.

  • Normalized Difference Builtup Index (NDBI) (swir - nir )/(swir + nir )
    The Normalized Difference Built-Up Index is used to extract the built-up areas in urban areas automatically.

  • Normalized Difference Water Index (NDWI) (nir - swir)/ (nir + swir)
    The Normalized Difference Water Index (NDWI) measures the change in water content of leaves using near-infrared (NIR) and short-wave infrared (SWIR) wavelengths, NDWI is used to analyze vegetation's water content because of its sensitivity to the water content of vegetation and water bodies.

  Source: https://www.gis-tutorials.net/blog/remote-sensing-indices-and-their-calculations-ndvi-ndwi-evi-savi-ndbi-mndwi-ibi/

• Implement index-based image classification using multi-threshold segmentation

  • predefine classes; water for NDWI, vegetation for NDVI, urban areas for NDBI
  • segmentation slider for respective thresholds
  • merging objects for respective classes
  • export resulting classes to vector format