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The major steps of image classification may include determination of a suitable classification system, selection of training samples, image preprocessing, feature extraction, selection of suitable classification approaches, post?classification processing, and accuracy assessment.

Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition.

Unsupervised and supervised image classification are the two most common approaches. However, object-based classification has gained more popularity because it's useful for high-resolution data.

Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition.

The major steps of image classification may include image preprocessing, feature extraction, selection of training samples, selection of suitable classification approaches, post-classification processing, and accuracy assessment.

In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order.

Here's how image classification works, step-by-step: Pre-processing: prepping your data. ... Object detection: locating objects within the image set. ... Object recognition and training: labeling located images. ... Object classification: your model is ready to classify your images. ... Connecting to an AI workflow. ... Implementation process: