The main goal of this lab is to perform object based classification using the program, eCognition which is a state-of-the-art object based image processing tool. Throughout this lab I used this program to segment an image into a homogeneous spatial and spectral cluster (object) as well as select the correct sample objects in order to train a nearest neighbor classifier. Finally I executed the object based classification output, correcting where it was necessary, from the nearest neighbor algorithm. In the previous labs I used different image classification methods on the same study area. Based on this I can then compare my results and determine which is the best and most accurate classification method for LULC (land use/land cover).
Methods
The first step in this lab exercise is to create a new project in eCognition Developer64. Once this was done I mixed the layers in order to adjust the image to appear in false color form. Next, I used the process tree to create image objects. I opened up the image tree and right-clicked inside the dialog to chose the option to 'append new' which is the first step towards creating a new process. After executing the first process I right-clicked on the first process I created and selected the option to 'insert child' where I edited the segmentation parameters. I selected the multiresolution segmentation from the list of algorithm and changed the shape to 0.2 and the compactness to 0.4, leaving the scale parameter at the default value of 10. After clicking 'execute', the process has been run and the multi-resolution segmentation image appears as the output (Fig. 1).
(Fig. 1) Multi-resolution segmentation image which was produced using the process tree tool.
The process of creating the image in figure 1 creates polygons which automatically select various LULC portions based on the brightness values. The next step was to create LULC classes. To do this I selected the option 'class hierarchy' from the classification tab. After right-clicking in the window to insert a class. I then entered in the following classes and selected the corresponding colors: forest (dark green), agriculture (pink), urban/built-up (red), water (blue), and green vegetation/shrub (light green).
Then it was time to declare the sample objects. To create the sample objects I opened the sample editor tool in the eCognition program. I then selected agriculture in the active class dialog in order to enter samples for that particular class. In order to collect the samples themselves, I selected a polygon in my image. The sample editor then marked the object's values with red arrows (Fig. 2). Once I decided they were good samples for the particular class I double clicked on the polygon and the sample editor changed their color to black showing that they were selected. I then added a few more samples to the agriculture class then changed the active class to one of my other feature classes and repeated the process.
(Fig. 2) The sample editor shows the selected polygon feature from the false color image on the right before it is double-clicked and becomes a sample object for that class.
Once I had collected samples for each of the LULC classes I used in this lab, I needed to apply the nearest neighbor classification. To do so, I went back to the process tree to 'append new' and created a classification process. Then I right-clicked on the classification process in order to insert a new child. Within this edit process window I then used the classification algorithm, selected all the classes as active and selected the 'execute' button to run the process. After the process was complete I was able to see my final output image which was a result of the object-based classification.
Results
The resulting output image of Eau Claire and Chippewa Counties using object-based classification was quite accurate (Fig. 3). While there was some difficulty in the original sampling which required me to do some manual editing it was a useful method. The best part about this method of LULC classification is that it the polygons are drawn for you rather than the user having to draw them themselves. This minimizes the errors in overlapping features and makes it easier for the user.
(Fig. 3) Output image showing the LULC of the Eau Claire and Chippewa Counties using object-based classification.
Sources
The data used in this lab exercise was provided from the following sources: Landsat satellite images from Earth Resources Observation and Science Center and USGS. All data was distributed by Dr. Cyril Wilson of the University of Wisconsin- Eau Claire.
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