I am interested in using Hidden Markov Models in the context of multi-sensors sequences in vegetation remote sensing.
The observation labels are the same as the state labels.
Each observation at a given time $t$ is classified and I want to use HMM in order to reduce classification errors.
For example, the states could be : FOREST, CROP, SOIL, WATER.
I have an associated transition matrix between those states.
Thanks to a sensor, I can observe a sequence of observations from which I want to infer the most likely sequence of hidden states.
Sequence of observations : FFFWFFFF
--> Sequence of states : FFFFFFFF
Now, each observation comes from a given sensor (or maybe ground data) and I am able to assign a given weight or confidence in this observation.
As a limiting case, let's imagine I'm 100% positive that the fourth observation is water because I was on the ground, and that the transitions from/to water are null.
I would like the algorithm to output :
--> Sequence of states : WWWWWWWW
I was thinking about using the following states:
- FOREST_SENSOR1, FOREST_SENSOR2,
and computing a new transition matrix but this does not seem right.
Thank you for your help!