Geography Reference
In-Depth Information
Thematic classes are categories of interest that the analyst tries to identify in the images, as
different types of crops, forests or species of trees, different types of rocks or geological
features, etc.
The result obtained in this study presents a double advantage. The first advantage is at the
level of accuracy in the identification of certain classes of information like the class sea. The
second one is in the identification of certain thematic classes within certain classes of
information and therefore a precise characterization can help in highlighting other
information, as for example, different canopies in terms of vegetation class information.
5.3.2. Case of mount Cameroon region
In Figures 10 and 11, we have uni-modal aspect of the histogram of the image after filtering.
This makes difficult any exploitation for segmentation. To overcome this drawback, it then
uses to texture images.
Figures 12 and 13 show them with now m-modal shape observed on the histogram of the
image texture. After several experiments, we retained eight cluster centers summarized in
table 3 with the color codes used for each class center.
For the Mount Cameroon region, the same approach as that used previously on the
mangrove is applied and the result is presented on Figure 15. The use of maps available and
research work on this site (Akono et al., 2005, 2006) are used to characterize the eight
thematic classes. The use of this information provides the classified image of the Mount
Cameroon region including the specification of each class of information is summarized on
the legend of Figure 15. As can be seen at the legend, we have nine more thematic classes
instead of eight. This is because when classifying all pixels are not classified. All unclassified
pixels were grouped in class vegetation. Furthermore, a broad thematic class (e.g. forest)
may contain multiple spectral classes with spectral variations. Using the example of the
forest, the spectral sub-classes can be caused by variations in age, species, tree density or
simply the effects of shadowing or variations in illumination. The analyst's job is to
determine the usefulness of different thematic classes and their correspondence to the
thematic classes useful.
6. Conclusion
The purpose of this study was the production of space maps with the synthetic aperture
radar (SAR) images. To achieve this, we proceed by adopting approaches optimized of
texture analysis of images, using the statistical parameters of Haralick generalized at the
order n. The approach is based on the concept of generic tree. It has the advantage of being
less time consuming calculation from the conventional approach which frequently uses co-
occurrence matrices for texture analysis and especially the processed images are generally
very large sizes. In classification, the approach relies on the concept of detecting "modes"
and "valleys" of histograms in a SAR image using classification of type unsupervised. For
each of the SAR images, the histogram of the convolution image obtained at base of texture
parameter is represented and approximated by a regression line called "signature" using
