The objects resulting from segmentation more closely resemble real-world features and produce cleaner classification results. Segmentation takes into account both color and shape characteristics when grouping pixels into objects. This is considered the more traditional classification method, and can result in a speckled effect in the classified image.Ĭlassification is performed on localized neighborhoods of pixels, grouped together with a process called segmentation. Characteristics of neighboring pixels are not considered in the pixel-based approach. There are two options for the type of classification to use for both supervised and unsupervised classification.Ĭlassification is performed on a per-pixel basis, where the spectral characteristics of the individual pixel determines the class to which it is assigned. All other pixels in the image are classified using the characteristics of the training samples. ![]() These sites are stored as a point or polygon feature class with corresponding class names for each feature, and they are created or selected based on user knowledge of the source data and expected results. Training samples are representative sites for all the classes you want to classify in your image. The outcome of the classification depends on the training samples provided. You provide the number of classes to compute, and the classes are identified and merged once the classification is complete. Pixels are grouped into classes based on spectral and spatial characteristics. Pixels or segments are statistically assigned to a class based on the ISO Cluster classifier. The outcome of the classification is determined without training samples. There are two options for the method you will use to classify your imagery. The parameters set here determine the steps and functionality available in the subsequent wizard pages. The first page is the Configure page, where you set up your classification project. The Classification Wizard is disabled if the active map is a 3D scene, or if the highlighted image is not a multiband image.Ĭlick the Classification Wizard button on the Imagery tab to open and dock the wizard. ![]() Select the raster dataset to classify in the Contents pane to display the Imagery tab, and be sure you are working in a 2D map. The Classification Wizard is found in the Image Classification group on the Imagery tab. These tools are the same ones included with the Classification Wizard. ![]() Experienced users can use individual tools available in the Classification Tools drop-down list in the Image Classification group. The Classification Wizard provides a guided workflow that is composed of best practices and a simplified user experience, so you can perform image classification without missing a step. The Classification Wizard guides users through the entire classification workflow from start to finish. Each step may be iterative, and the process requires in-depth knowledge of the input imagery, classification schema, classification methods, expected results, and acceptable accuracy. ![]() The workflow involves multiple steps to progress from preprocessing to segmentation, training sample selection, training, classifying, and assessing accuracy. Image classification is the process of extracting information classes, such as land-cover categories, from multiband remote sensing imagery.
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