Lab 10: Object-based Classification

Background and Goal

In this lab, we learn how to utilize object-based classification in eCognition. This method of classification is state-of-the-art, and uses both spectral and spatial information to classify the land surface features of an image. The first of three steps in this process is to segment an image into different homogenous clusters (referred to as objects). This is then followed by selecting training samples to be used in a nearest neighbor classifier, an algorithm which is then used with the samples to create a classification output.

Methods

As stated previously, the first step in object-based classification is to segment the image into different objects. This is done by incorporating a false color infrared image into a multiresolution segmentation algorithm within eCognition’s ‘Process Tree’ function. For this classification, a scale parameter of 10 was used. This determines how detailed or broad the segments are. After this process is executed, it categorizes the different features in the image, splitting them as different objects. Figure 1 shows the different objects that were generated for the Eau Claire area.

Figure 1: An example of the segmentation that was created to differentiate between the different 'objects' of the image.

It is then necessary to create classes for the object-based classification. In the ‘Class Hierarchy’ window of eCognition, five different classes were inserted. The classes we used were Forest, Agriculture, Urban/built-up, Water, and Green vegetation/shrub. Each of these classes was assigned a different color. After this, the nearest neighbor classifier was applied to the classes, with the mean pixel value selected for the objects’ layer value. This will be the classifying algorithm for the process. Once this is complete, samples are selected in a much similar way to supervised classifications. Four objects in each class were selected and used as samples for the nearest neighbor classification. A new process was then created in the process tree, and the classification algorithm was assigned. Once this process was executed, the image becomes classified. It was then necessary to manually edit the image to change objects that had been falsely classified. The results were then able to be exported as an ERDAS Imagine Image file, to be further analyzed in a more familiar program.

Results

Figure 2 shows the result of the classification. This classification process was fairly simple to use, and produced beautiful results. Urban/built-up is still slightly under predicted, but overall the image looks quite accurate.

Figure 2: The final result of the object-based classification technique.

Sources

Earth Resources Observation and Science Center. (2000). [Landsat image of the Eau Claire and Chippewa Counties]. United States Geological Survey. Obtained from http://glovis.usgs.gov/