Remotior Sensus Update: Version 0.4

I'm glad to announce the update of Remotior Sensus to version 0.4.
This new version add several new features such as clustering, raster editing and raster zonal stats. Following the complete changelog:
  • Added tool "Band clustering" for unsupervised K-means classification of bandset
  • Added tool "Raster edit" for direct editing of pixel values based on vector
  • Added tool "Raster zonal stats" for calculating statistics of a raster intersecting a vector.
  • Improved the NoData handling for multiprocess calculation
  • In "Band clip", "Band dilation", "Band erosion", "Band sieve", "Band neighbor", "Band resample" added the option multiple_resolution to keep original resolution of individual rasters, or use the resolution of the first raster for all the bands
  • In "Cross classification" fixed area based accuracy and added kappa hat metric
  • In "Band combination" added option no_raster_output to avoid the creation of output raster, producing only the table of combinations
  • In "Band calc" replaced nanpercentile with optimized calculation function
  • Improved extraction of ROIs in "Band classification"
  • Minor bug fixing and removed Requests dependency

For example the following command allows for the unsupervised K-means classification of a bandset.
import remotior_sensus
rs = remotior_sensus.Session(n_processes=2, available_ram=1000)
# create BandSet
catalog = rs.bandset_catalog()
file_list = ['L8_B2.tif', 'L8_B3.tif', 'L8_B4.tif', 'L8_B5.tif',
             'L8_B6.tif', 'L8_B7.tif']
catalog.create_bandset(file_list)
clustering = rs.band_clustering(
                input_bands=catalog.get(1), output_raster_path='output.tif',
                algorithm_name='minimum distance', class_number=10, 
                max_iter=10, seed_signatures='random pixel'
            )
These tools work with parallel processes, in particular, input raster (or multiple rasters) are divided into pieces and the minimum number of sections according to the number of processes and available RAM (see the following figure). In the end, the sections are merged to create a new raster for the entire output. 


Many of these enhancements will also be implemented in the Semi-Automatic Classification Plugin for QGIS.

For any comment or question, join the Facebook group or GitHub discussions about the Semi-Automatic Classification Plugin and Remotior Sensus.

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