在 R 中显示 2 个栅格之间的异同答案

【问题标题】：Displaying similarities and differences between 2 rasters in R在 R 中显示 2 个栅格之间的异同
【发布时间】：2020-08-10 09:04:13
【问题描述】：

我正在尝试创建一个栅格，以显示随机森林预测和神经网络预测在哪里达成一致和不一致。每个栅格都是对 6 个土地类别的预测，我想创建一个新栅格来比较两者之间的比较，其中一致预测的单元格为绿色，不同预测的单元格为红色。

这是我的两种预测方法及其栅格的代码：

trainD <- dataAll[dataAll$sampleType == "train",]
validD <- dataAll[dataAll$sampleType == "valid",]

# Random Forest
# use 'Caret' package to find the optimal parameter settings
tc <- trainControl(method = "repeatedcv", 
                   number = 10, # number 10 fold
                   repeats = 10) # number of repeats
rf.grid <- expand.grid(mtry=1:sqrt(9)) # use 9 bc we have 9 bands

# train the random forest model to the Sentinel-2 data through Caret
trainD <- na.omit(trainD) #omit points in cloud areas from training  points
rf_model <- caret::train(x = trainD[,c(5:13)],  #digital number data
                         y = as.factor(trainD$landcID),  
                         method = "rf",  
                         metric="Accuracy",  
                         trainControl = tc,  #use parameter tuning 
                         tuneGrid = rf.grid)  #parameter tuning grid
#check output
rf_model

# Change name in raster stack to match training data
names(allbandsCloudf) <- c("B2","B3","B4","B5","B6","B7","B8","B11","B12")
# Apply the random forest model to the Sentinel-2 data
rf_prediction <- raster::predict(allbandsCloudf, model=rf_model)
#view predictions
plot(rf_prediction)
# landcover class names
landclass

# set up categorical colors for each class using hex codes
landclass$cols <-c("#a6d854","#8da0cb","#66c2a5",
                   "#fc8d62","#ffffb3","#ffd92f")
# make plot and hide legend
plot(rf_prediction, #random forest prediction
     breaks=seq(0,6), #number of landclasses
     col=landclass$cols , 
     legend=FALSE, axes=FALSE) #hide legend


# Neural Networks
# set up grid
nnet.grid <- expand.grid(size = seq(from = 16, to = 28, by = 2), 
                         decay = seq(from = 0.1, to = 0.6, by = 0.1))
# train the model
nnet_model <- caret::train(x = trainD[,c(5:13)], 
                           y = as.factor(trainD$landcID),
                           method = "nnet", 
                           metric= "Accuracy", 
                           trainControl = tc, 
                           tuneGrid = nnet.grid,
                           trace=FALSE)
# view the training summary
nnet_model
# apply the neural network model to the Sentinel-2 data
nnet_prediction <- raster::predict(allbandsCloudf, model=nnet_model)
# make plot and hide legend
plot(nnet_prediction, #plot the neural network predictions
     breaks=seq(0,6), #number of landclasses
     col=landclass$cols ,
     legend=FALSE) #hide the legend

【问题讨论】：

欢迎来到 Stack Overflow @tpants12，这个社区是一个问答社区。在这篇文章中不清楚你的问题是什么。你能明确你的问题吗？问候。
欢迎来到 SO。您的问题和您的问题究竟是什么？
@vpz 和desertnaut 谢谢！你们两个是绝对正确的，我完全忘了直接问我的问题。菜鸟失误！我的问题是“有人对如何使用两个预测栅格完成创建新栅格有任何建议吗？”幸运的是，我能够找到自己问题的解决方案，所以我发布了一个答案，以防其他人试图解决类似的问题

标签： r machine-learning neural-network random-forest r-raster

【解决方案1】：

我能够找出我自己问题的答案！我只是使用了叠加，然后将颜色设置为我想要的颜色并添加了一个图例。

如果有人尝试解决类似问题，请查看以下代码：

diff <- overlay(rf_prediction, nnet_prediction, fun=function(a,b) return(a==b))
plot(diff,
     col=c('#FFE4E1','#228B22'),
     legend=FALSE,
     axes=FALSE)
legend("left", legend=c("Agree", "Disagree"),
       col=c("#228B22", "#FFE4E1"), pch = 15, cex=0.8)

【讨论】：