After each area of reflection and each rocky area have unique values, they can be treated separately.įigure 3: Results of the Region Group tool. The Region Group tool reclassifies the image by grouping adjacent pixels with a common value together. This is largely due to the pixel values of the reflections in the water resembling those of the highly reflective rocks on land. While some classes are now clearly either land or water, many are still found in both regions. Separate the Feature from the Surrounding Area Majority filter and boundary clean tools.įigure 2: The classified imagery after use of generalization tools and reclassification. Isolates more stray pixels which can now be eliminated by re-running the The remainingĬlasses should be left separate until later. Reclassified together, as can those which are all water. I found that by the fourth run there were no longer any significant improvements being made to the image, so I then ran the Boundary Clean tool to tidy up the few remaining stray pixels.īy this point some of the classes are exclusively either water or land. The image is quite pixelated, even though only five classes were used, but running the Majority Filter tool a few times eliminated many of the smaller clusters of pixels. It is usually best to use the lowest number possible, which will save you some extra work later on. Some images may require more, others fewer. This image required five classes to adequately capture the major land/ocean difference. I have chosen to use Iso Cluster Unsupervised classification (new at 10.0) because the value of the extra control offered by a supervised classification is outweighed by the time required to designate the training areas.įigure 1: The results of a 5-class Iso Cluster Unsupervised classification In this case, where the objective is to classify the entire image as either ‘island’ or ‘ocean’, either supervised or unsupervised classification could be used to distinguish between the island and the ocean. A more detailed explanation of image classification and the differences between supervised and unsupervised classification can be found in the help topic What is image classification. Unsupervised classifications don’t require the designation of training areas and are therefore quicker and easier to perform, but there is much less control over how the pixel values are grouped. The training areas used in a supervised classification can be time consuming to create, but they offer significantly more control over the classification process. When the classes are defined by an automatic process without the user’s input, the classification is considered “Unsupervised”. When the classes are user-defined, usually by using a series of training areas, the classification is considered “Supervised”. Image classification, in its most general form, is the process of sorting or arranging pixels in an image into classes or clusters. If the feature is small and simple, digitizing it by hand may be an option, but I prefer to let ArcGIS do the heavy work whenever possible. Here I will create a polygon which will eventually serve as a mask to isolate the island.Įxtracting a single feature from the surrounding image while preserving the edge detail can be a daunting task. By removing the area covered by water from the image, I was able to focus the classification on just the vegetation itself. The ability to create an accurate classification of the entire image is complicated by the effect of sunlight reflecting off the waves in the ocean. The first two steps in this process, georeferencing the images and mosaicking them together, were covered in the first and second posts. The final goal of the project was to produce a detailed classification of the island’s vegetation from a series of digital aerial photographs. These images are from a project I recently completed looking at the structure of a seabird colony off the coast of Nova Scotia, Canada, and are representative of the less-than-ideal imagery many of us have to work with regularly. This is the third in a series of blog posts that will cover some tips and tricks for performing the following operations on a series of aerial images using ArcGIS 10.0:
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