![]() ![]() Don’t worry, modifying how the image is rendered (displayed) won’t affect the classification results.įigure 2: Histogram-Equalization stretched image Applying a stretch renderer such as Histogram-Equalization can make it easier to identify areas of different vegetation. The Image Analysis Window offers several simple ways to adjust the display of an image in order to enhance the variations. If you are working with an RGB image it can be difficult to distinguish subtle differences in the vegetation. So… how do you choose good training areas? ![]() The help documentation covers several of these topics in detail, including creating, evaluating and managing training samples. While you can certainly have too few, I really don’t think you can have too many good quality training areas. ![]() The key? – training areas – lots and lots of good training areas. While I didn’t get a meaningful classification using this method, it took less than 2 minutes to run and I was able to identify problem areas where I will need to focus my training areas to get a successful Maximum Likelihood Classification.įigure 1: Results of a 30 class IsoCluster Unsupervised classificationĭig Deeper with Supervised ClassificationĪ Maximum Likelihood Classification can be time consuming to prepare, but the results can be fabulous. Even using as many as 30 classes, the maximum number where I could still make sense of the results, the software could not distinguish between three of the major vegetation types due to variations in the foliage. While, as seen in the previous post the Iso Cluster Unsupervised Classification did a great job at separating the island from the ocean, it really struggled with grouping the vegetation into meaningful classes. Identify Trouble Areas using Unsupervised Classification To reduce the potential number of misclassifications, the area covered by ocean was excluded from the image using the Extract by Mask tool and the outline of the island created in the previous post. Initially I tried to classify the entire image, but reflections in the water and waves breaking on shore made it difficult for the software to construct an accurate classification, even using hundreds of training areas. An excellent resource on classification in ArcGIS10.0 is available here.Ī high quality vegetation classification can be a powerful tool in vegetation and habitat analysis , as it can provide a lot of information about a large area with relatively little work. In this final section, I’ll focus on classification techniques including identifying areas requiring more focused training areas, what makes a good training area, and how you can quickly and easily clean up your classified image. The Georeferencing and mosaicking of the imagery were covered in previous posts, as was creating a polygon mask of the island. 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 fourth 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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