Classification of the Hyper-Spectral and LiDAR Imagery using R (mostly). Part 1: Result Evaluation
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Introduction
There was the EEEI Data Fusion Contest this spring. This year they wanted people to elaborate about hyper-spectral (142-bands imagery) and LiDAR data. The resolution of the data-set was about 5 m. There were 2 nominations: best classification and the best scientific paper.
I work with high-resolution imagery quite often, but classification is a very rear task for me though. I thought that this contest was a great opportunity to develop my skills. And not just a classification skills, but R skills as well… I decided to participate in best classification contest, and to use R for the most part.
I learned a lot and I will share my knowledge with you in a series of posts starting with this one. And like in some great novels, I will start from the very end – evaluation of my results.
 |
| Results of my classification (created in R, designed in QGIS) |
Contest results
…are available here. As you may notice, I’m not on the list of the 10 best classification 😉 But there is almost unnoticeable 0.03% difference between my result (85.93% accuracy) and the result of the 10-th place. Not a bad result for a relatively inexperienced guy, don’t you think? And I know, that I could have done better – I had 99% prediction accuracy for the training samples. It’s funny, but my classification map looks better than map that took 7-th place!
How to evaluate classification using R
Due to I was not on the top ten list, I had to evaluate the result on my own. The organisers finally disclosed evaluation samples and I got a chance for a self assessment. So we have a set of .shp-files – each contains ground-truth polygons for one of the 16 classes and a classification map. We need to accomplish 3 goals:
- Create a visual representation of missclassification.
- Assess accuracy.
- Create a confusion matrix.
- Visualise classification map using EEEI colour palette.
Lets get a palette!
 |
| Official EEEI palette |
To extract colour values from the palette above you may use GIMP. But I used a widget that every KDE-user (Linux) should have by default. You can probe and save colour values from any part of your screen. Quite useful!
 |
| ‘Color picker’ in work |
Now let’s see the code for our tasks.
Load needed libraries
library(rgdal) library(raster) library(reshape2) library(caret) library(ggplot2)
Load and process data
# get classification raster
ras <- raster('~/GIS/IEEE_contest_2013/2013_IEEE_GRSS_DF_Contest/raster.tif',
verbose = T)
# get list of shp-files for evaluation
shapes <- list.files(path = '~/GIS/IEEE_contest_2013/2013_IEEE_GRSS_DF_Contest/roi', pattern = '*shp')
# a list for accuracy assessment
accuracy_list <- list()
# create an empty dataframe to be filled vith evaluation results
# field names are not arbitrary!!!
eval_df <- data.frame(variable = character(),
value = character())
# create an empty dataframe to be used for plotting
# field names are not arbitrary!!!
plot_data <- data.frame(variable = character(),
value = character(),
Freq = integer())
for (f_name in shapes) {
# delete '.shp' from the filename
layer_name <- paste(sub('.shp', '', f_name))
class <- readOGR("~/GIS/IEEE_contest_2013/2013_IEEE_GRSS_DF_Contest/roi",
layer = layer_name,
verbose = F)
# extract values from raster
probe <- extract(ras, class)
# replace class numbers with names
samples <- list()
for (lis in probe) {
for (value in lis) {
if (value == 0) {c_name <- Unclassified
} else if (value == 1) {c_name <- 'Healthy grass'
} else if (value == 2) {c_name <- 'Stressed grass'
} else if (value == 3) {c_name <- 'Synthetic grass'
} else if (value == 4) {c_name <- 'Trees'
} else if (value == 5) {c_name <- 'Soil'
} else if (value == 6) {c_name <- 'Water'
} else if (value == 7) {c_name <- 'Residential'
} else if (value == 8) {c_name <- 'Commercial'
} else if (value == 9) {c_name <- 'Road'
} else if (value == 10) {c_name <- 'Highway'
} else if (value == 11) {c_name <- 'Railway'
} else if (value == 12) {c_name <- 'Parking Lot 1'
} else if (value == 13) {c_name <- 'Parking Lot 2'
} else if (value == 14) {c_name <- 'Tennis Court'
} else if (value == 15) {c_name <- 'Running Track'}
samples <- c(samples, c = c_name)
}
}
# make layer_name match sample name
if (layer_name == 'grass_healthy') {layer_name <- 'Healthy grass'
} else if (layer_name == 'grass_stressed') {layer_name <- 'Stressed grass'
} else if (layer_name == 'grass_syntethic') {layer_name <- 'Synthetic grass'
} else if (layer_name == 'tree') {layer_name <- 'Trees'
} else if (layer_name == 'soil') {layer_name <- 'Soil'
} else if (layer_name == 'water') {layer_name <- 'Water'
} else if (layer_name == 'residental') {layer_name <- 'Residential'
} else if (layer_name == 'commercial') {layer_name <- 'Commercial'
} else if (layer_name == 'road') {layer_name <- 'Road'
} else if (layer_name == 'highway') {layer_name <- 'Highway'
} else if (layer_name == 'railway') {layer_name <- 'Railway'
} else if (layer_name == 'parking_lot1') {layer_name <- 'Parking Lot 1'
} else if (layer_name == 'parking_lot2') {layer_name <- 'Parking Lot 2'
} else if (layer_name == 'tennis_court') {layer_name <- 'Tennis Court'
} else if (layer_name == 'running_track') {layer_name <- 'Running Track'}
# create a dataframe with classification results
df <- as.data.frame(samples)
dfm <- melt(df, id = 0)
dfm['variable'] <- layer_name
# add data to evaluation dataframe
eval_df <- rbind(eval_df, dfm)
# assess accuracy of current class
mytable <- table(dfm)
dmt <- as.data.frame(mytable)
total_samples <- 0
correct_predictions <- 0
for (i in 1:nrow(dmt)) {
predict_class <- toString(dmt[i,2])
pc_frequency <- dmt[i,3]
if (predict_class == layer_name) {
correct_predictions <- dmt[i,3]
}
total_samples <- total_samples + pc_frequency
}
accuracy <- round(correct_predictions/total_samples, 2)
accuracy_list <- c(accuracy_list, c = accuracy)
# append data for plotting
plot_data <- rbind(plot_data, dmt)
}
Create a fancy graph (that is shown on the map)
# create facets plot
EEEI_palette <- c('#D4D4F6',
'#5F5F5F',
'#710100',
'#00B300',
'#007900',
'#014400',
'#008744',
'#D0B183',
'#BAB363',
'#DAB179',
'#737373',
'#A7790A',
'#00EA01',
'#CA1236',
'#00B9BB')
plot_data <- plot_data[order(plot_data$variable),]
p = ggplot(data = plot_data,
aes(x = factor(1),
y = Freq,
fill = factor(value)
)
)
p <- p + facet_grid(facets = . ~ variable)
p <- p + geom_bar(width = 1) +
xlab('Classes') +
ylab('Classification rates') +
guides(fill=guide_legend(title="Classes"))+
scale_fill_manual(values = EEEI_palette)+
ggtitle('Classification Accuracy')+
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())
p
Let't finally get our accuracy result!
# accuracy assessment
fin_accuracy <- mean(unlist(accuracy_list))
fin_accuracy <- paste(round(fin_accuracy*100, 2), '%', sep = '')
print(paste('Total accuracy:', fin_accuracy), sep = ' ')
[1] "Total accuracy: 85.93%"
Confusion matrix
# confusion matrix creation true <- as.factor(eval_df$variable) predict <- as.factor(eval_df$value) confusionMatrix(predict, true)
Enjoy statistics!
Confusion Matrix and Statistics
Reference
Prediction Commercial Healthy grass Highway Parking Lot 1
Commercial 850 0 14 151
Healthy grass 0 868 0 0
Highway 0 0 718 14
Parking Lot 1 41 0 54 641
Parking Lot 2 0 0 19 73
Railway 0 0 11 44
Residential 155 0 191 0
Road 0 0 20 107
Running Track 0 0 0 0
Soil 0 0 1 0
Stressed grass 0 61 0 0
Synthetic grass 0 0 0 0
Tennis Court 0 0 0 0
Trees 0 117 0 0
Water 0 0 0 0
Reference
Prediction Parking Lot 2 Railway Residential Road Running Track
Commercial 0 0 74 2 0
Healthy grass 0 0 0 0 0
Highway 0 9 0 0 0
Parking Lot 1 104 8 0 11 0
Parking Lot 2 155 4 0 3 0
Railway 2 917 11 61 0
Residential 0 40 918 0 0
Road 12 14 0 930 0
Running Track 0 0 1 0 465
Soil 6 2 0 27 1
Stressed grass 0 11 4 0 0
Synthetic grass 0 0 0 0 3
Tennis Court 0 3 0 0 0
Trees 0 0 10 0 0
Water 0 0 0 0 0
Reference
Prediction Soil Stressed grass Synthetic grass Tennis Court Trees
Commercial 0 0 0 0 0
Healthy grass 0 14 0 0 34
Highway 0 0 0 0 0
Parking Lot 1 0 0 0 0 0
Parking Lot 2 0 0 0 0 0
Railway 0 0 0 0 0
Residential 0 1 0 0 0
Road 14 0 0 0 0
Running Track 0 0 0 0 0
Soil 1040 0 0 0 0
Stressed grass 0 931 0 0 17
Synthetic grass 0 0 503 0 0
Tennis Court 0 0 0 245 0
Trees 0 85 0 0 1004
Water 0 0 0 0 0
Reference
Prediction Water
Commercial 0
Healthy grass 3
Highway 0
Parking Lot 1 22
Parking Lot 2 0
Railway 0
Residential 0
Road 0
Running Track 0
Soil 0
Stressed grass 0
Synthetic grass 0
Tennis Court 0
Trees 0
Water 118
Overall Statistics
Accuracy : 0.859
95% CI : (0.853, 0.866)
No Information Rate : 0.088
P-Value [Acc > NIR] : <2e-16
Kappa : 0.847
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: Commercial Class: Healthy grass Class: Highway
Sensitivity 0.8126 0.8298 0.6984
Specificity 0.9780 0.9953 0.9979
Pos Pred Value 0.7791 0.9445 0.9690
Neg Pred Value 0.9820 0.9839 0.9724
Prevalence 0.0872 0.0872 0.0857
Detection Rate 0.0709 0.0724 0.0599
Detection Prevalence 0.0910 0.0767 0.0618
Class: Parking Lot 1 Class: Parking Lot 2
Sensitivity 0.6223 0.5556
Specificity 0.9781 0.9915
Pos Pred Value 0.7276 0.6102
Neg Pred Value 0.9650 0.9894
Prevalence 0.0859 0.0233
Detection Rate 0.0535 0.0129
Detection Prevalence 0.0735 0.0212
Class: Railway Class: Residential Class: Road
Sensitivity 0.9097 0.9018 0.8994
Specificity 0.9883 0.9647 0.9848
Pos Pred Value 0.8767 0.7034 0.8478
Neg Pred Value 0.9917 0.9906 0.9905
Prevalence 0.0841 0.0849 0.0862
Detection Rate 0.0765 0.0766 0.0776
Detection Prevalence 0.0872 0.1088 0.0915
Class: Running Track Class: Soil
Sensitivity 0.9915 0.9867
Specificity 0.9999 0.9966
Pos Pred Value 0.9979 0.9656
Neg Pred Value 0.9997 0.9987
Prevalence 0.0391 0.0879
Detection Rate 0.0388 0.0867
Detection Prevalence 0.0389 0.0898
Class: Stressed grass Class: Synthetic grass
Sensitivity 0.9030 1.0000
Specificity 0.9915 0.9997
Pos Pred Value 0.9092 0.9941
Neg Pred Value 0.9909 1.0000
Prevalence 0.0860 0.0420
Detection Rate 0.0777 0.0420
Detection Prevalence 0.0854 0.0422
Class: Tennis Court Class: Trees Class: Water
Sensitivity 1.0000 0.9517 0.82517
Specificity 0.9997 0.9806 1.00000
Pos Pred Value 0.9879 0.8257 1.00000
Neg Pred Value 1.0000 0.9953 0.99789
Prevalence 0.0204 0.0880 0.01193
Detection Rate 0.0204 0.0837 0.00984
Detection Prevalence 0.0207 0.1014 0.00984
As you may see, the main source of misclassification are Parking Lot 1 and Parking Lot 2. The accuracy for other classes is above 90%, and it is great. Frankly, I still don't understand what is the difference between Parking Lots 1 and 2... Official answer was that Parking Lot 2 is cars (isn't detecting them using 5 m resolution imagery is a questionable task???)... But it seems that it is something else. It is hard to classify something that you don't understand what it is...
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