Buzzwords

In my last post I wrote about Pragmatic Remote Sensing. Today I would like to introduce another concept: Buzz Remote Sensing.

OK, this is just a joke but anyway, I guess one could consider using the following approach: use google to make choices about algorithms.

You may be aware of Google Trends which allows you to compare search words in terms queries at Google. For instance, you can compare the trends of "SVM" (in blue) and "maximum likelihood" (in red). Here they are:


So, there is no doubt about what to choose for a classification. Let's see about clustering. "Mean Shift" (blue) and "K-means" are plotted below:

So if you want to be ahead of time, use the Mean Shift so your papers get easily published.

And we could go on. Imagine you have to build a processing chain for cartographic data base updating using satellite images. The main steps would be:

1. Image to data base registration (mutual information? feature-based?)
2. Image segmentation (watershed? morphological profiles? mean shift?)
   
3. Feature extraction (geometric invariants? haralik textures? wavelet coefficients?)
   
4. Object recognition on images (spatial reasoning?, template matching?)
5. Supervised classification (neural networks? kernel methods?)
6. Change detection (image differencing? MAD?)
7. Saving the vector layers (shapefile? KML?)
   

Who wants to implement the most fashionable processing chain out there?