Changing the processing paradigm?

No I am not going to write about compressive sensing. I am sorry.

The paradigm I want to write about is the one currently used for mapping applications from remote sensing imagery. This way of turning images into maps can be resumed as ortho-analysis-GIS, that is, you get the image and convert it into a map projection, then you analyze it (segmentation, classification, etc.) in order to produce vector layers and finally you import these vector layers into a GIS in order to perform geospatial analysis (eventually fusing with existing vector maps) and you produce the final map.

This works OK, but is not the most efficient way of working. If you look at the final map, how much information really came from the images? How may pixels were really useful for producing this information? The answer is usually "not much".

Now look at the computation time needed by all the processing steps in the map production chain. Can you guess what is the most expensive step? With current high resolution images, the ortho-rectification step is the most time consuming.

One solution to this could be to ortho-rectify only the interesting area for the application. The drawback of this approach is that usually, you need to process the image (detect the pertinent features, changes, etc.) before you know where is the interesting information.

In this case, the solution, would be to process the image before ortho-rectification. There is one main problem to this : many modern software tools for segmentation and classification are not very good at processing huge images. Even if they were good at it, you would need to process the whole image before ortho-rectification.

The thing is that often, the existing maps tell you where the interesting things are likely to be found, but since your maps are "ortho", you still need to ortho-rectify your image before processing.

Also, the geospatial reasoning step is made at the end of the processing, inside the GIS tool, which usually knows very little about image processing and so.

So it seems that the paradigm cited above (which could also be named ERDAS-Definiens-ArcGIS, for example), although useful has real drawbacks for efficiency. And I am not talking about import/export and format issues.

In order to be really efficient, we would need a tool which would allow us to send the existing shapefile or KML maps on top of the image in sensor geometry, perform some geospatial reasoning up there, segment and classify only the areas of interest (still in sensor geometry), produce vector data and finally send only the useful information down to the map projection.

Hey, I finally nearly wrote about compressive sensing, didn't I?

To finnish: don't tell anybody, but it seems there is a free software out there which is able to do what I have just wrote. Well the PostGIS interface is not yet ready, but it is on its way.