# Hidden Markov Models - Weight observations

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,
• etc.

and computing a new transition matrix but this does not seem right.