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Image classification refers to the task of assigning classes—defined in a land cover and land use classification system, known as the schema—to all the pixels in a remotely sensed image.
The output raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two methods of classification: supervised and unsupervised. They both can be either object-based or pixel-based.
Image classification can be a lengthy workflow with many stages of processing. In ArcGIS Pro, the classification workflows have been streamlined into the Classification Wizard so a user with some knowledge in classification can jump in and go through the workflow with some guidance from the wizard. There are also individual classification tools for more advanced users that may only want to perform part of the classification process.
The Classification Wizard guides users through the entire classification workflow. It provides a solution comprised of best practices and a simplified user experience to guide users through the classification process in an efficient manner.
Segmentation is a key component of the object-based classification workflow. This process groups neighboring pixels together that are similar in color and have certain shape characteristics.
The Training Samples Manager page is divided into two sections: the schema management section at the top, and training samples section is at the bottom. A classification schema is used to organize all of the features in your imagery into distinct classes. A training sample is an area you have defined into a specific class, which needs to correspond to your classification schema.
You can classify your data using unsupervised or supervised classification techniques. This step processes your imagery into the classes, based on the classification algorithm and the parameters specified.
After you have performed a supervised classification you may want to merge some of the classes into more generalized classes. You are limited to the classes which are the parent classes in your schema.
After you have performed an unsupervised classification, you need to organize the results into meaningful class names, based on your schema.
Accuracy assessment uses a reference dataset to determine the accuracy of your classified result. Accuracy is represented from 0 - 1, with 1 being 100 percent accuracy.
After you classify an image, you will probably encounter small errors in the classification result. You can make edits to individual features or objects.
Supervised and unsupervised classification
Depending on the interaction between the analyst and the computer during classification, there are two methods of classification: supervised and unsupervised.
Supervised classification is where you decide what class categories you want to assign pixels or segments to. These class categories are referred to as your classification schema. After the classification is complete, you will have to go through the resulting classified dataset and reassign any erroneous classes or class polygons to the proper class based on your schema.
Unsupervised classification is where you let the computer decide which classes are present in your image based on statistical differences in the spectral characteristics of pixels. After the unsupervised classification is complete, you need to assign the resulting classes into the class categories within your schema.
Object-based and pixel-based
There are two options for the type of classification method that you choose: pixel-based and object-based.
Pixel-based is a traditional approach that decides what class each pixel belongs in on an individual basis. It does not take into account any of the information from neighboring pixels. It can lead to a salt and pepper effect in your classification results.
The object-based approach groups neighboring pixels together based on how similar they are in a process known as segmentation. Segmentation takes into account color and the shape characteristics when deciding how pixels are grouped. Because this approach essentially averages the values of pixels and takes geographic information into account, the objects that are created from segmentation more closely resemble real-world features in your imagery and produces cleaner classification results.