Remote sensing systems (such as LIDAR) allow us to measure a plethora of variables on the earth’s surface and in the air. This includes temperature, wind, vegetation, clouds, ice and many more. However, the relationship between the variable to be measured and the signal received can be complex, as radiation passing through the atmosphere is scattered and absorbed by clouds and molecules in the atmosphere. Attempting to determine this relationship using theory alone may be arduous. Machine learning is a natural alternative.
Let \(v\) denote some variable we wish to measure (perhaps on the earth’s surface), and \(s\) the signal received by the sensor (possibly located on a satellite). We wish to find a relationship
\[v = f(s),\]
that will allow us to convert the properties of the signal, which may contain data from several frequency bands, to the properties we actually wish to measure. Suppose we have gathered a large number of data pairs \((s_i,v_i)\). We can do this be recording the signal received \(s_i\) when the sensor is pointed at a location whose properties \(v_i\) are already known. We then use these data points to train or teach a neural network.
Ideally, the data used for fitting is only a subset of the total amount of data collected, so that the neural network can be cross-validated against data that was not used in the fitting process.
Machine learning has been applied to remote sensing in many practical applications including:
- Measuring the chlorophyll concentration in the ocean
- Classifying vegetation
- Classifying cloud types
- Measuring precipitation
- Identifying snow cover
Of course, remote sensing is only one of an unlimited number of applications of machine learning. Whenever you wish to determine a relationship present in your data that may be too complex to determine using scientific theory, machine learning is an exciting alternative.