In the past few labs pixel based classification was used to do supervised and unsupervised classification. This lab will be exploring cutting edge object based classification. This lab will use the eCognition software to do this. Object based classification integrates both spectral and spatial information in order to classify objects. This lab will go through the process of doing two different methods of object based classification, random forest, and support vector.
Methods:
eCognition is a ground breaking software that is capable of very advanced classification. This is the software that will be used in this lab for both support vector and random forest.
To begin the process the user needs to bring in a satellite image. In this case a 2000 landsat image was used of the Eau Claire and Chippewa Valley area. The user then selects the band combination they wish to use, for this lab a 432 false color IR combination will be used. The next step is segmentation and sample collection. Segmentation is breaking the image up into small pieces, these are the objects. The goal is for the objects is to contain only one land use/land cover class. To create these objects the Process Tree tool needs to be used. In process tree, the user begins by naming the process and then what kind of process needs to be run. The image below is what this looks like.
A few settings are very important when generating objects. In this case multiresolution segmentation was used with a scale parameter of 9, shape of .3 and compactness of .5. This creates objects on the image. The image below is what the resulting objects look like.
The next step is to create classes. This is done by using Class > Class Heirarchy. Once this tab is ipen the user simple right clicks and adds the classes by naming them and selecting the color. The next step is to collect samples for each of the classes. This is similar to what is done when doing supervised classification. The user selects the class in their class heirarchy then double clicks an object that contains only the area they wish to include in the class. The following table is the minimum amount of samples that should be taken for each class.
This is now the point that the process changes for random forest and support vector. The first process that will be done is random tree. To begin, random forest needs to be trained. To do this a variable is created for the random forest (RF) classifier. This is done by creating another line in the process tree. The following image is the parameters given to the classifier.
A few set of rules are also given to the classifier, these are given below.
The classifier is now ran. The following image is the initial classified image.
A lot of the urban areas were not classified correctly so these areas were manually changed.
The next method is the support vector machines method. This method used a different algorithm when grouping pixels. The only difference when doing this method changing the method to support vector machines. The same objects and samples were used. The results from both of these methods can be found in the results section.
The final image to be classified is a Unmanned Aerial Systems image. This is an image taken from the University of Wisconsin Eau Claire drone fleet. The process to classify this image is the same as doing satellite images. In this case, the SVM method was used. The only big difference for the UAS image was the scale parameter used. The scale parameter used had to be put all the way up to 200 because the image is at such a large scale. The resulting classified image can be seen in the results section.
Results:
Sources:
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