Object-based Classification

- Introduction -

The last classification technique of the semester, object-based classification, is a fairly new method which attempts to succeed where per-pixel or sub-pixel classifiers fail. Pixel-based classifiers only account for spectral properties in an image when determining informational classes which often results in the salt and pepper effect and similar pixelated landscape patterns. By accounting for spatial properties, like distance, texture, and shape, an object-based classifier results in a more natural looking and often times more accurate classified image. Object-based classification segments an image into areas based on both spectral and spatial homogeneity criteria. An analyst can then classify specific objects and use them as training samples to classify the entire image. In this lab exercise, a Landsat TM image of Eau Claire and Chippewa Counties, WI was classified through object-based classification and a nearest neighbor algorithm.

- Methods -

Object-based classification was performed with eCognition software. A new project was created and the image of Eau Claire and Chippewa Counties, WI was imported into the project. The image was segmented by navigating to Process > Process Tree and creating a new pair of parent and child processes. Multi-resolution segmentation was chosen as the algorithm, a value of 0.2 was given for the shape scale parameter, and a value of 0.4 was given for the compactness scale parameter. Individual layer weights could also be modified at this point, however, the default values of 1 were accepted for this exercise. Clicking 'Execute' in the Edit Process window initiated the image segmentation.

Figure 1: Example of the image objects
created after image segmentation

Before image objects were selected as training samples, the desired informational classes needed to be created and the classification algorithm needed to be defined. Five classes were created in the Class Hierarchy window, opened by navigating to Classification > Class Hierarchy. Nearest neighbor was selected as the classification algorithm and was modified by navigating to Classification >  Nearest Neighbor > Edit Standard NN Feature Space. Certain image objects were then selected for training samples based on visual interpretation by first navigating to Classification > Samples > Select Samples. To classify an image object, the desired informational class was selected in the Class Hierarchy window and then the image object was double-clicked.

Once the training samples were collected, a new pair of parent and child processes was created in Process Tree window for the classification. The active classes were chosen under Algorithm parameters in the edit process window and 'execute' was chosen. The classification was performed after 'execute' was chosen and the result was modified by selecting new training samples as needed, performing manual editing, and then re-running the classification. The final classified image was then exported and made into a map using ArcMap.

- Results -

Map 1: The final classified image produced through object-based classification

- Discussion -

The classified image produced through object-based classification was a vast improvement compared to the classified images produced early in the semester through pixel-based classification. The salt and pepper effect of inaccurate urban classification throughout the image, common in previous classifications, was eliminated. The overall time to classify the image was also drastically reduced. No bare ground class was used for this classification which did overestimate agricultural land but the other classes represented the landscape fairly well. Spectral properties of the image were given more influence for this classification because much of the area is more natural and less urbanized. For study areas that consist of mostly urban landscape, more emphasis should be placed on shape/spatial properties for a better classification.

- Conclusion -

Object-based classification produced the most natural looking classified image of all the methods used throughout the semester and took less time to produce. The benefits of object-based classification are blatant and it is a powerful (relatively) new method to determine land cover/land use. Many insecurities in accuracy of classified images produced through pixel-based methods were reduced by using the object-based method.

- Sources -

Earth Resources Observation and Science Center, USGS. Landsat TM imagery.