Principal Component analyses

I can not get PCA to finish processing. I’ve let it run through the night.
I have resampled the files to 10 before processing. I’m using the latest SNAP
The files are S2A -MSI-L1C.

how many bands were used as input for the PCA?

I’ve tried from 2 bands, to all bands in many different combinations. Also get same
result with Classification, supervised and unsupervised. Sometimes in classification I get
a solid color or stair like color images corresponding to the number of vectors I used.
But 95% of the time its a blank image. My vectors always have at least 10,000 pixels each and have 7 or more vectors. Frustrating
Thanks for any help

A PCA does not need any vectors, could you please clarify?

yes your right about the PCA. I thought I would explain the other image problems I am having
with classification to maybe help figure out what I am doing wrong.

so do you get an error or does it just take very long?

You should try if it works on a very small subset first. It’s not uncommon that PCAs take tremendous time, especially for large rasters and many features.

no I do not get errors

This is the report I get with small subset.

RandomForest classifier newClassifier

Cross Validation
Number of classes = 4
class 0.0: WATER
accuracy = 0.5580 precision = 0.2600 correlation = 0.4263 errorRate = 0.4420
TruePositives = 520.0000 FalsePositives = 1480.0000 TrueNegatives = 2270.0000 FalseNegatives = 730.0000
class 1.0: GRASS
accuracy = 0.6452 precision = 0.2380 correlation = 0.3423 errorRate = 0.3548
TruePositives = 238.0000 FalsePositives = 762.0000 TrueNegatives = 2988.0000 FalseNegatives = 1012.0000
class 2.0: LAVA ROCK
accuracy = 0.6552 precision = 0.2630 correlation = 0.3510 errorRate = 0.3448
TruePositives = 263.0000 FalsePositives = 737.0000 TrueNegatives = 3013.0000 FalseNegatives = 987.0000
class 3.0: IRON RED
accuracy = 0.6444 precision = 0.2360 correlation = 0.3417 errorRate = 0.3556
TruePositives = 236.0000 FalsePositives = 764.0000 TrueNegatives = 2986.0000 FalseNegatives = 1014.0000

Using Testing dataset, % correct predictions = 25.1400
Total samples = 10000
RMSE = 1.6454786537661312
Bias = -0.30000000000000004

Distribution:
class 0.0: WATER 2500 (25.0000%)
class 1.0: GRASS 2500 (25.0000%)
class 2.0: LAVA ROCK 2500 (25.0000%)
class 3.0: IRON RED 2500 (25.0000%)

Testing feature importance score:
Each feature is perturbed 3 times and the correct predictions are averaged The importance score is the original correct prediction - average
rank 1 feature 13 : B12 score: tp=0.0000 accuracy=0.0000 precision=0.0000 correlation=0.0000 errorRate=0.0000 cost=0.0000 GainRatio = 0.0000
Warning: rank <= featureBandList.length

does the result look alright? If so, it is simply a matter of file size and computing time. Maybe you can switch to a computer with higher capabilities for this task.

NO. There is no image displayed

can you please give the ID of the product you are using?

Software 7.0.3
S2A_MSIL2A_20190627T180921_N0212_R084_T12SUH_20190628T002159_resampled

I downloaded your product, resampled it, made a subset and then applied PCA (3 components). It is important that you actively select the reflectance bands as only inputs:

Otherwise you will impute the patterns of the mask bands and quality indicators into the PCA.

All good.
Thanks so much for your help

did you solve your problem? If yes, how?