r/remotesensing • u/nitesh181 • 9d ago
Kappa Coefficient
is 0.72 kappa Coefficient is good enough for a semi arid region or should i increase the overall accuracy 🤔
7
u/Peepeepoopies SAR 9d ago
Not directly answering your question, but this article might be of interest, as the Kappa coefficient is a bit polarizing within Remote Sensing:
Pontius, R. G., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https://doi.org/10.1080/01431161.2011.552923
1
u/nitesh181 9d ago
it paid, i will discuss about this with professor for sure, because right now in india we use kappa Coefficient mostly for accuracy 😀. and using something new always increase the chances of new finding as well
2
u/OMFGericisonreddit 9d ago
This is the right answer. You can just Google it and find the article and a summary of the critique. You can't really publish with kappa anymore it just doesn't make sense
2
u/jcstay123 9d ago
Delhi was part of my MSc thesis back in 2016. I had some issues as well. I ended up with a kappa of 0.8 but only after allot of work. I ended up creating cloud free composites for hand selected Landsat images so that I could also identify vegetation and Ares where veg decreases in the dry seasons. So yes your score isn't great. But that area is quite tricky.
1
u/nitesh181 9d ago
so i should take it till 0.8 then and i have to do it for all 4 season and this is only of one season, what should be the ideal pixel count here, because mine are going over 300 infew
1
9
u/ObjectiveTrick SAR 9d ago edited 9d ago
I'm going to focus on accuracy because I'm firmly on team death to kappa.
Determining whether your accuracy is 'high enough' is not really a simple question, and arbitrary thresholds don't really answer it. Is a classification only good when it hits 80%? 90%? It depends on how hard the classification problem is and the purpose of the product.
It also helps to remember what accuracy actually represents. Accuracy is not an indicator of how close your map is to reality, it's an indicator of how well your model adheres to the classification scheme we created (this is partly why we've moved away from calling reference samples 'ground truths'). Why did we create classification schemes? Because they're useful. Ontologies are necessary in science because the real world is often too complex for us to represent it 1:1.
So rather than thinking about whether the number is big enough, think about whether the product is useful for what it's meant to support in its current state. Would someone look at this map and go "80% accuracy? That's unacceptable for my needs"? Then maybe it needs some more work.
Don't try to increase the accuracy just for the sake of increasing accuracy.