In this script, we will evaluate the performance of the WATG technique for region classification in PolSAR textures.

Importing the packages

# Load some packages: 
if(!require(caret)) install.packages("caret")
if(!require(MLmetrics)) install.packages("MLmetrics")

FGPE with \(\alpha = 1\)

n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))
Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)
Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityFGPET1A1.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions
split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)
x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])
x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])
Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)

Creating KNN model and predicting

set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)
pred = predict(knnFit, newdata = x_validation)
xtab = table(pred, y_validation)
confusionMatrix(xtab)
Confusion Matrix and Statistics

        y_validation
pred     Forest Sea Urban
  Forest      5   2     0
  Sea         1  10     0
  Urban       0   0     6

Overall Statistics
                                          
               Accuracy : 0.875           
                 95% CI : (0.6764, 0.9734)
    No Information Rate : 0.5             
    P-Value [Acc > NIR] : 0.0001386       
                                          
                  Kappa : 0.8033          
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: Forest Class: Sea Class: Urban
Sensitivity                 0.8333     0.8333         1.00
Specificity                 0.8889     0.9167         1.00
Pos Pred Value              0.7143     0.9091         1.00
Neg Pred Value              0.9412     0.8462         1.00
Prevalence                  0.2500     0.5000         0.25
Detection Rate              0.2083     0.4167         0.25
Detection Prevalence        0.2917     0.4583         0.25
Balanced Accuracy           0.8611     0.8750         1.00
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
Accuracy:  0.875  Recall:  0.7142857  Precision:  0.8333333  F1-Score:  0.7692308

FGPE with \(\alpha = 0.5\)

n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))
Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)
Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityFGPET1A05.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions
split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)
x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])
x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])
Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)

Creating KNN model and predicting

set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)
pred = predict(knnFit, newdata = x_validation)
xtab = table(pred, y_validation)
confusionMatrix(xtab)
Confusion Matrix and Statistics

        y_validation
pred     Forest Sea Urban
  Forest      5   0     0
  Sea         1  12     0
  Urban       0   0     6

Overall Statistics
                                          
               Accuracy : 0.9583          
                 95% CI : (0.7888, 0.9989)
    No Information Rate : 0.5             
    P-Value [Acc > NIR] : 1.49e-06        
                                          
                  Kappa : 0.9322          
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: Forest Class: Sea Class: Urban
Sensitivity                 0.8333     1.0000         1.00
Specificity                 1.0000     0.9167         1.00
Pos Pred Value              1.0000     0.9231         1.00
Neg Pred Value              0.9474     1.0000         1.00
Prevalence                  0.2500     0.5000         0.25
Detection Rate              0.2083     0.5000         0.25
Detection Prevalence        0.2083     0.5417         0.25
Balanced Accuracy           0.9167     0.9583         1.00
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
Accuracy:  0.9583333  Recall:  1  Precision:  0.8333333  F1-Score:  0.9090909

AAPE with A = 1

n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))
Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)
Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityAAPED3T1A1.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions
split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)
x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])
x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])
Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)

Creating KNN model and predicting

set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)
pred = predict(knnFit, newdata = x_validation)
xtab = table(pred, y_validation)
confusionMatrix(xtab)
Confusion Matrix and Statistics

        y_validation
pred     Forest Sea Urban
  Forest      3   3     0
  Sea         3   9     0
  Urban       0   0     6

Overall Statistics
                                          
               Accuracy : 0.75            
                 95% CI : (0.5329, 0.9023)
    No Information Rate : 0.5             
    P-Value [Acc > NIR] : 0.01133         
                                          
                  Kappa : 0.6             
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: Forest Class: Sea Class: Urban
Sensitivity                 0.5000      0.750         1.00
Specificity                 0.8333      0.750         1.00
Pos Pred Value              0.5000      0.750         1.00
Neg Pred Value              0.8333      0.750         1.00
Prevalence                  0.2500      0.500         0.25
Detection Rate              0.1250      0.375         0.25
Detection Prevalence        0.2500      0.500         0.25
Balanced Accuracy           0.6667      0.750         1.00
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
Accuracy:  0.75  Recall:  0.5  Precision:  0.5  F1-Score:  0.5

AAPE with A = 0.5

n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))
Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)
Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityAAPED3T1A05.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions
split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)
x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])
x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])
Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)

Creating KNN model and predicting

set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)
pred = predict(knnFit, newdata = x_validation)
xtab = table(pred, y_validation)
confusionMatrix(xtab)
Confusion Matrix and Statistics

        y_validation
pred     Forest Sea Urban
  Forest      2   2     0
  Sea         4  10     0
  Urban       0   0     6

Overall Statistics
                                          
               Accuracy : 0.75            
                 95% CI : (0.5329, 0.9023)
    No Information Rate : 0.5             
    P-Value [Acc > NIR] : 0.01133         
                                          
                  Kappa : 0.5862          
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: Forest Class: Sea Class: Urban
Sensitivity                0.33333     0.8333         1.00
Specificity                0.88889     0.6667         1.00
Pos Pred Value             0.50000     0.7143         1.00
Neg Pred Value             0.80000     0.8000         1.00
Prevalence                 0.25000     0.5000         0.25
Detection Rate             0.08333     0.4167         0.25
Detection Prevalence       0.16667     0.5833         0.25
Balanced Accuracy          0.61111     0.7500         1.00
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
Accuracy:  0.75  Recall:  0.5  Precision:  0.3333333  F1-Score:  0.4
---
title: "Analysis of amplitude information techniques in classification of regions"
author: "Eduarda Chagas"
date: "May 7, 2020"
output:
  html_notebook: default
  pdf_document: default
---

In this script, we will evaluate the performance of the WATG technique for region classification in PolSAR textures.

###Importing the packages

```{r}
# Load some packages: 
if(!require(caret)) install.packages("caret")
if(!require(MLmetrics)) install.packages("MLmetrics")

```

###FGPE with $\alpha = 1$

```{r}
n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))

Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)

Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityFGPET1A1.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions


split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)

x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])

x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])

Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)
```

####Creating KNN model and predicting
```{r}
set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)

pred = predict(knnFit, newdata = x_validation)

xtab = table(pred, y_validation)
confusionMatrix(xtab)
```

```{r}
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
```

###FGPE with $\alpha = 0.5$

```{r}
n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))

Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)

Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityFGPET1A05.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions


split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)

x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])

x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])

Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)
```

####Creating KNN model and predicting
```{r}
set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)

pred = predict(knnFit, newdata = x_validation)

xtab = table(pred, y_validation)
confusionMatrix(xtab)
```

```{r}
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
```

###AAPE with A = 1

```{r}
n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))

Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)

Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityAAPED3T1A1.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions


split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)

x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])

x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])

Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)
```

####Creating KNN model and predicting
```{r}
set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)

pred = predict(knnFit, newdata = x_validation)

xtab = table(pred, y_validation)
confusionMatrix(xtab)
```

```{r}
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
```

###AAPE with A = 0.5

```{r}
n.total = 160
regions = c(rep("Forest",40), rep("Sea",80), rep("Urban", 40))

Entropy.Complexity = data.frame("Entropy" = numeric(n.total), 
                                "Complexity" = numeric(n.total),
                                "Region" = character(n.total), 
                                stringsAsFactors=FALSE)

Entropy.Complexity.csv = read.csv(file="../../Data/EntropyComplexityAAPED3T1A05.csv", 
                                  header=TRUE, sep=",")
Entropy.Complexity$Entropy = Entropy.Complexity.csv[,1]
Entropy.Complexity$Complexity = Entropy.Complexity.csv[,2]
Entropy.Complexity$Region = regions


split = 0.85
trainIndex = createDataPartition(Entropy.Complexity$Region, p = split, list = FALSE)

x = data.frame(Entropy.Complexity$Entropy[trainIndex], Entropy.Complexity$Complexity[trainIndex])
y = factor(Entropy.Complexity$Region[trainIndex])

x_validation = data.frame("Entropy" = Entropy.Complexity$Entropy[-trainIndex], "Complexity" = Entropy.Complexity$Complexity[-trainIndex])
y_validation = factor(Entropy.Complexity$Region[-trainIndex])

Entropy.Complexity = data.frame("Entropy" = Entropy.Complexity$Entropy[trainIndex], 
                                "Complexity" = Entropy.Complexity$Complexity[trainIndex],
                                "Region" = Entropy.Complexity$Region[trainIndex], 
                                stringsAsFactors=FALSE)
```

####Creating KNN model and predicting
```{r}
set.seed(123)
ctrl = trainControl(method="repeatedcv", number = 10, repeats = 10)
knnFit = train(Region~., data = Entropy.Complexity, method = "knn", 
               trControl = ctrl, 
               preProcess = c("center","scale"), 
               tuneLength = 20)

pred = predict(knnFit, newdata = x_validation)

xtab = table(pred, y_validation)
confusionMatrix(xtab)
```

```{r}
cat("Accuracy: ", Accuracy(pred, y_validation), " Recall: ", Recall(pred, y_validation), " Precision: ", Precision(pred, y_validation), " F1-Score: ", F1_Score(pred, y_validation), "\n")
```
