-
Notifications
You must be signed in to change notification settings - Fork 0
/
NabuQtrlyClass.R
132 lines (116 loc) · 4.61 KB
/
NabuQtrlyClass.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
library(raster)
library(rgdal)
library(sp)
library(randomForest)
library(RStoolbox)
library(e1071)
library(reshape2)
library(cluster)
library(terra)
library(rpart)
library(caTools)
library(fields)
library(dplyr)
library(purrr)
library(magick)
library(caret)
library(ggplot2)
library(rasterVis)
setwd("")
set.seed(1)
#load the 2019 geomedians
NabuQ1 <- stack("")
NabuQ2 <- stack("")
NabuQ3 <- stack(".tif")
NabuQ4 <- stack("tif")
summary(NabuQ1)
s2bands <- list("green", "red", "blue", "nir", "swir_1", "swir_2", "SMAD", "NDVI", "NDWI", "BSI")
NabuQ1 <- setNames(NabuQ1, s2bands)
NabuQ1plot <- plotRGB(NabuQ1, r=2, g=1, b=3, stretch="hist",axes=TRUE)
ggRGB(NabuQ1, r=7, g=NULL, b=NULL)
summary(NabuQ1)
NabuQ2 <- setNames(NabuQ2, s2bands)
NabuQ3 <- setNames(NabuQ3, s2bands)
NabuQ4 <- setNames(NabuQ4, s2bands)
#StackQtrs
NabuQtrs <- stack(NabuQ1,NabuQ2,NabuQ3,NabuQ4)
#load training polygons
NabuTrain <- readOGR(".shp")
summary(NabuTrain)
plot(NabuTrain)
NabuTrain$Class <- as.factor(NabuTrain$id)
classes <- c("Irrigated","Other")
classesdf <- data.frame(classnum = c(1,2), classnames=classes)
levels(NabuTrain$Class) <- c("Irrigated","Other")
#' Divide data into training and validation
Nabu_smp_size <- floor(0.8*nrow(NabuTrain))
Nabu_train_ind <- sample(seq_len(nrow(NabuTrain)), size=Nabu_smp_size)
NabuTrain_tr <- NabuTrain[Nabu_train_ind,]
NabuTrain_val <- NabuTrain[-Nabu_train_ind,]
#' RStoolbox superclass NORMALLY SKIP THIS SECTION (it is an alternative rf modelling method)
#superclassNabu <- superClass(NabuQtrs, NabuTrain, valData = NULL, responseCol = "id",
# nSamples = 100000, polygonBasedCV = TRUE, trainPartition = 0.8,
# model = "rf", tuneLength = 2, kfold = 5, minDist = 1,
# mode = "classification", predict = FALSE, predType = "raw",
# filename = NULL, verbose=TRUE, overwrite = TRUE)
#superclassNabu
#superpredictNabu <- predict(superclassNabu, NabuQtrs)
#plot(superpredictNabu)
#superclassimp <- varImp(superclassNabu$model)
#plot(superclassimp, type = 0)
#varImpPlot(superclassNabu$model, type=1)
#' Generate point samples on training data
pt_Nabutrain <- spsample(NabuTrain_tr, 80000, type='random')
pt_Nabutrain$Class <- over(pt_Nabutrain, NabuTrain_tr)$Class
pt_Nabutrain <- vect(pt_Nabutrain)
#' Extract spectral vals on training data
xy_train <- as.matrix(geom(pt_Nabutrain)[,c('x','y')])
df_Nabutrain <- extract(NabuQtrs, xy_train)
head(df_Nabutrain)
data_Nabutrain <- data.frame(Class=pt_Nabutrain$Class, df_Nabutrain)
summary(data_Nabutrain)
data_Nabutrain <- na.omit(data_Nabutrain)
#' Generate point samples on validation data
pt_Nabuval <- spsample(NabuTrain_val, 20000, type='random')
pt_Nabuval$Class <- over(pt_Nabuval, NabuTrain_val)$Class
pt_Nabuval <- vect(pt_Nabuval)
#' Extract spectral vals on validation data
xy_val <- as.matrix(geom(pt_Nabuval)[,c('x','y')])
df_Nabuval <- extract(NabuQtrs, xy_val)
head(df_Nabuval)
data_Nabuval <- data.frame(Class=pt_Nabuval$Class, df_Nabuval)
summary(data_Nabuval)
data_Nabuval <- na.omit(data_Nabuval)
#' Create training and validation data
NabuTraining = data_Nabutrain
NabuValid = data_Nabuval
#' RF classifier
Nabu_Random_Forest_Classification <- randomForest(x=NabuTraining[-1], y=as.factor(NabuTraining$Class), ntree=500, importance=TRUE)
plot(Nabu_Random_Forest_Classification)
Nabu_Random_Forest_Classification
#' Variable importance
NabuQtrVIP <- varImpPlot(Nabu_Random_Forest_Classification, type=1)
NabuQtrImp <- as.data.frame(NabuQtrVIP)
NabuQtrImp$varnames <- rownames(NabuQtrImp)
rownames(NabuQtrImp) <- NULL
NabuQtrImp
NabuQtrVIPgg <- ggplot(NabuQtrImp, aes(x=reorder(varnames, MeanDecreaseAccuracy), y=MeanDecreaseAccuracy))+
geom_point()+geom_segment(aes(x=varnames,xend=varnames,y=0,yend=MeanDecreaseAccuracy))+
xlab("Variable")+ylab("Mean Decrease Accuracy")+coord_flip()
NabuQtrVIPgg
grid.arrange(NabuQtrVIPgg, NabuQtrVIPgg, ncol=2)
grid.arrange(LungQtrVIPgg, ncol=2)
#' Prediction stats and confusion matrix
NabuPrediction <- predict(Nabu_Random_Forest_Classification, newdata=NabuValid)
NabuconMat <- confusionMatrix(NabuPrediction, as.factor(NabuValid$Class))
NabuconMat
#' Look at the map
NabuMap <- predict(NabuQtrs, Nabu_Random_Forest_Classification, filename="RF_Nabu.img", type="response",
index=1, na.rm=TRUE, progress="window", overwrite=TRUE)
plot(NabuMap)
NabuMap <- ratify(NabuMap)
rat <- levels(NabuMap)[[1]]
rat$legend <- classesdf$classnames
levels(NabuMap) <- rat
NabuLevelMap <- levelplot(NabuMap)
NabuLevelMap