Category: Artificial Intelligence

Image classification is a fundamental task that helps to classify and comprehend an image as a whole. The main motive of image classification is to classify the image by assigning it to a specific label.

Usually, Image Classification refers to images in which only one object appears, and that is the only object analyzed. In contrast, object detection involves both classification and localization tasks and is used to analyze more realistic cases in which multiple objects may exist in an image.

Supervised vs Unsupervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image representative of specific classes and then direct the image processing software to use these training sites as references for classifying all other pixels in the image.

Classification is a process done with a multi-step workflow, and the Image Classification toolbar has been developed to provide an integrated environment for performing classifications with the tools.

The toolbar not only helps with the workflow for performing unsupervised and supervised classification, but it also contains additional functionality for analyzing input data, creating training samples and signature files, and determining the quality of the training samples and signature files.

Supervised Image Classification

Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Data Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

In supervised classification, you select representative samples for each land cover class. The software then uses these “training sites” and applies them to the entire image.

For supervised image classification, you first create training samples. For example, you mark urban areas by marking them in the image. Then, you would continue adding training sites representative in the entire image.

Unsupervised Image Classification

Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, the capability to analyze the quality of the clusters and access to classification tools

In unsupervised classification, pixels are first grouped into “clusters” based on their properties. Then, you classify each cluster with a land cover class.

Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.

Which one is better?

No doubt, unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of the area required, but you must be able to identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.

On the other hand, one of the disadvantages of unsupervised classification is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and labeling the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use the same class information when moving from one image to another.

Both have their own advantages and disadvantages, but for machine learning projects, supervised image classification is better for making the objects recognized with better accuracy. Overall, object-based classification outperformed both unsupervised and supervised pixel-based classification methods. Depending on the compatibility of the Gen AI model or machine learning algorithm, an image classification process is followed to classify the images with better accuracy and quality object detection.