Pragmatic Remote Sensing

As I pointed out in a previous post, in the computer science field, may attempts have been done in the recent years to improve software quality by being rational in the process.

Being rational here is just being pragmatic in order to use techniques that work for you, your team and your project.

When I see many remote sensing scientists present their results and their research objectives, I wonder if the pragmatic way could help improve the results and the benefits for the final users.

Of course, fundamental research has to be conducted in the signal and image processing fields. New theoretical approaches are needed to overcome many challenges in remote sensing image processing. That being said, I often see people trying to develop very complex (and complicated) theories and techniques in order to solve problems which may find a solution using state of the art computer vision algorithms.

In the recent years, the increase of geometric resolution of sensors has made available images which are more similar to data used in robotics or industrial vision than to classical satellite imagery.

The problems we need to solve for this kind of data may benefit from solutions developed in other image processing application fields. For instance, counting objects with simple shapes (rectangular buildings, vehicles, isolated trees) is a problem which has been solved in industrial vision or in biological image processing (counting cells, for instance).

Another point that we should bare in mind is that today's computer power allows to perform in real time operations which took minutes not long ago. This opens up the field of semi-automatic and interactive approaches for image processing. You can see one example of implementation of an object segmentation application using the ORFEO Toolbox here.

The idea here is to use the operator in the processing steps where the task is too hard for the computer. You will agree with the fact that this is a good complement to the approach of using the computer for tedious tasks for the operator.

As a conclusion, I would say that pragmatic remote sensing should be applied as the first step when trying to solve a real problem for an end user. Only if a straight-forward solution (using state of the art computer vision algorithms, choosing the good balance between manual and automatic approaches) can not be found, a more complex approach should be investigated.

Of course, pragmatic remote sensing won't add a line to your publications list, but may solve a real problem.