Using a satelite image RGB (VHR) for discrimination of habitat for wetland Oualidia (Morocco) from a hybrid methodology based on object-oriented classification with the K-means algorithm

The mapping of wetland habitats requires image data of high spatial resolution in order to establish the precise contours and space occupies a specific habitat. However, the spectral deficiency of high resolution images accentuates the problems of proximity and spectral mixing between image objects, which makes them very sensitive classification operations in such environments. The present work offers a solution based on an unsupervised approach to habitat classification of the wetland lagoon of Oualidia and its surroundings. To do this, a picture RBV (1m) covering the study area was segmented from the software GRASS, followed by extraction optimal segments as polygons from QGIS software. The partitioning algorithm K-means was used for classification of selected polygons in the respective classes, and this using three (3) discrimination criteria (color, shape, and size). The objective is to propose a solution in the discrimination of different types of wetland habitats from a poor image spectral resolution, but harboring a very high spatial resolution. As such, the algorithm permits to classify the different habitats with an accuracy of 0.88 according to the index of Kappa.