Supervised Classification using ArcGIS

To classify the image, the Maximum Likelihood Classification tool should be used. This tool is based on the maximum likelihood probability theory. It assigns each pixel to one of the different classes based on the means and variances of the class signatures (stored in a signature file). Unsupervised vs Supervised Classification in Remote Sensing Previously, we’ve explored digital image classification techniques like unsupervised classification, supervised classification and object-based. Also, we’ve gone into great detail how to do object-based image classification. Now, it only makes sense to practice supervised and unsupervised classification with some examples. Supervised Classification in Remote Sensing When you run a supervised classification, you typically go through the following 3 steps: Step 1 Enable Image Analysis Toolbar Step 2 Select training areas Step 3 Generate signature file Step 4 Classify Unsupervised Classification in Remote Sensing Unsupervised classification is different because it does not provide sample classes. First, the user identifies how many classes to generate and which bands to use. Next, the software then clusters pixels into the set number of classes. Finally, the user then identifies the land cover classes. Unsupervised Classification Steps: Generate clusters Assign classesStep 1 Activate Spatial Analyst Extension Step 2 Generate clusters Step 3 Assign classes Classifying Images with Supervised and Unsupervised Methods This sums up some of the basics for unsupervised classification in remote sensing. We generated unknown classes (isodata) using iso clusters. Next, the user identified each cluster with land cover classes. Some manual editing may be necessary if there is confusion between classes. Put these steps to practice and generate some land cover of your very own. Select training areas Generate signature file Classify التصنيف المراقب باستخدام برنامج ارك جي اي اس arcgis التصنيف المراقب والغير مراقب باستخدام برنامج ارك جي اي اس