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Image classification refers to the task of assigning classesdefined in a land cover and land use classification system, known as the schemato all the pixels in a remotely sensed image. The output raster from image classification can be used to create thematic maps.

Image Classification is the Basis of Computer Vision It forms the basis for other computer vision problems. Image classification applications are used in many areas, such as medical imaging, object identification in satellite images, traffic control systems, brake light detection, machine vision, and more.

Image classification groups the pixels in different classes. The image classification includes- image acquisition, image pre-processing, image segmentation. Some image classification methods are- Support Vector Machine (SVM), Artificial Neural Network (ANN) and Decision Tree (DT).

Image classification is the process of assigning land cover classes to pixels. For example, classes include water, urban, forest, agriculture, and grassland. The 3 main types of image classification techniques in remote sensing are: Unsupervised image classification. Supervised image classification.

Image classification is the process of assigning land cover classes to pixels....The 3 main types of image classification techniques in remote sensing are:Unsupervised image classification.Supervised image classification.Object-based image analysis.Oct 29, 2021

The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground. Image classification is perhaps the most important part of digital image analysis.

Image classification, at its very core, is the task of assigning a label to an image from a predefined set of categories. Practically, this means that our task is to analyze an input image and return a label that categorizes the image. The label is always from a predefined set of possible categories.