Monthly Archives: June 2014

Geomatica OrthoEngine Automated Mosaicking

I have used Geomatica OrthoEngine Automated Mosaicking for many years.  If the imagery to be mosaicked do not have too much radiometric differences,  Dynamic Mosaicking in Image Analysis Windows of ArcGIS is the way to go. It is the easiest and fastest way. However, ArcGIS does not have good color balancing strategy. When dramatic radiometric differences existing among images, I prefer to use Geomatica OrthoEngine Automated Mosaicking, which has a much better color balancing capability compared to ArcGIS, ENVI, and Erdas Imagine.   I just discovered recently that Geomatica OrthoEngine Automated Mosaicking can have a Water Mask, which is used to exclude pixels from statistics for calculating the color balancing of the image. “Water and clouds are the most common features that are excluded, because they will have detrimental effects on the color balancing of ground pixels. Large water bodies do not usually balance well, which is a general industry issue”.  A detailed tutorial of Geomatica OrthoEngine Automated Mosaicking can be found in the link below:

http://www.pcigeomatics.com/pdf/Automatic_Mosaicking_2013.pdf

Here are the key steps of Geomatica OrthoEngine Automated Mosaicking:

  1. Generate the Water Mask. If you already have the water layer in vector format, you can convert it to raster format.
  2. Create a bitmap layer. Just right click the “New Area” in Table of Content in Geomatica Focus, select “New Bitmap Layer”
  3. Transfer water layer to Bitmap Layer.  Focus ->File->Utility->Transfer Layers.
  4. Follow the tutorial to create mosaicking image with Water Mask.

Automatic_Mosaicking_PCI_2013

before

after

How to edit the rule sets of decision tree in R

Sometimes it is desirable to edit the rule sets derived from the training data before making prediction for the test data. There are many packages for decision tree classification in R.  Today I explored the way to edit the rule sets.  See the sample codes and illustration below.  The way to do it is to save the rule sets as ASCII file, edit the rule sets in the ASCII file, then import back to R to do prediction.See the sample codes and illustration below.

####################################################

library(C50)

data(churn)

ruleModel <- C5.0(churn ~ ., data = churnTrain, rules = TRUE)       #construct the rule sets

ruleModel

summary(ruleModel)               #print out the rule sets

ruleText = ruleModel$rules

write(ruleText, file=”ruleText.txt”)              #save the rule sets to ASCII file, edit the rule sets as needed.

ruleModel$rules = “”                                      #empty the original rule sets

predict(ruleModel,newdata=churnTest)                     #no rules in the model, will get error on this line

ruleText = paste(readLines(“ruleText.txt”),collapse=”\n”)           #imported the modified rule sets to R

ruleModel$rules=ruleText                                        #assign the modified rules back to the rule model

predict(ruleModel,newdata=churnTest)                #do prediction based on the new rule sets

####################################################

rules

 

Department Banner Design

The banner I designed for Department of Geography, University of Cincinnati has been produced, finally. Thank you for all the comments and critiques during the design process!  It is a long journey, but I am glad I made it.

DepartmentBanner20140602_140021

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