 This video will demonstrate a workflow in which a pixel-based unsupervised classification is used to map land cover from landsat imagery. Unsupervised pixel-based classification approaches work by partitioning the input imagery into a set of output classes based only on the spectral values. Because they're only making use of the spectral information, they are limited in their utility. They cannot make use of spatial information such as size, shape, or even texture. One can use the isocluster unsupervised classification geoprocessing tool to perform an unsupervised classification, but a more streamlined approach is to use the image classification wizard. To do this, first select the image dataset in the table of contents that you want to classify, and then from the imagery tab, choose classification wizard. The first step is to configure the classifier. Under classification method, I'm choosing unsupervised, and for the classification type, I'm going to use a pixel-based. Now, I already have an existing classification schema, so I'm going to load that here. If you don't have a classification schema, you can always select default and modify it later. Once you've finished entering the configuration parameters, you can click Next. This will move to the next step in the image classification wizard, which is called train. For an unsupervised classification, the train phase is where you'll enter some of the key parameters. The most common parameter you'll want to modify is the maximum number of classes. This is the total maximum number of output classes that you can have in the resulting classification. You want this to be higher than the number of classes you need, but note that this is the maximum and it may not be achieved. I'm going to adjust the maximum number of classes to 10 and leave all the other defaults in place. Clicking Run will produce the initial classification. You'll want to review your output here, and you may want to go back to the previous stage and make some adjustment to the input parameters, such as adjusting the number of classes. If you're happy with the output, simply click Run in the Classified window to move to the next phase. This will produce another raster data set with the same number of classes, and in the Classified window, you can now click Next to transition to the next phase. In the Assign class phase, I'm going to assign a LAN cover class to each one of the categories in my unsupervised classification. It's important to note that although classes may be specially dissimilar and then have unique values in the unsupervised classification, they could belong to the same LAN cover class. Prior to assigning classes to the unsupervised classification, you may want to make some modifications to your classification schema. For example, you can right-click and edit the properties of the schema, giving it a new name and entering descriptive information. You can make modifications to individual classes, adjusting their name, color, and default numerical value. No two classes should have the same numerical value. You can also add or remove classes from the existing schema. Then finally, if you're happy with your classification schema and are considering reusing it in the future, be sure to save it using one of the Save options. Assigning the appropriate LAN cover class to each one of your unsupervised categories may take some time. You'll want to compare your unsupervised classification to at least your input imagery and perhaps even to some other reference data that you may have access to. To associate each LAN cover class with its corresponding unsupervised category, you'll want to select the class in the Assign Class dialog, click on the Assign Class button, and then click on a pixel belonging to that class in the unsupervised classification. The unsupervised class assignment information is updated in both the Assign Class dialog and in the Table of Contents. Clicking on a class in the Assign Class table will highlight that particular class in the unclassified raster, helping you understand what pixels belong to that particular category. As was mentioned earlier, one or more unsupervised categories could have the same LAN cover class. You'll want to continue this process of associating LAN cover classes with unsupervised categories until all of your unsupervised categories have an associated LAN cover class. As I'm working with Landsat data, which is at an angle, I even have a background category that I need to consider for my classification. Once you've completed the class association process, you can click Next to move to the reclassifier stage. For an unsupervised classification, it's unlikely that you'll need to apply any reclassification routines, so you can click Run to finalize your classification. This produces the final classified raster, which is stored inside your project to your database. You have the option of removing any intermediate products from your Table of Contents to clean up your ArcGIS project. Unsupervised classification is an easy and straightforward way to getting it feature extraction. However, it should generally only be applied to multispectral imagery, and in those cases where the LAN cover classes can be clearly separated by only spectral information.