▸ Unattended classification:

0
classification:

This technique is very useful in tracking urban sprawl, land degradation and desertification, forest degradation, river morphology, valleys and a range of other topics….
This technique is divided into two ways:
1) Observer rating
2) Unattended classification
In the controlled classification, the user or image analyzer “supervises” the pixel rating process. The user determines the different pixel values or spectral signatures that must be associated with each category. This is done by selecting representative sample locations for a pre-known category in nature.

These samples are called representative sites and the computer algorithm then uses spectral signatures from these representative sites to categorize the entire image; ideally, category classifications should not overlap for this and for the accuracy of the classification process the boundaries of sample areas should be somewhat far from the limits of the category in which the sample is taken.

In the controlled classification, the largest impairment occurs before the actual classification process. Once the rating is turned on, the result is an objective image with classified categories that correspond to the types of land cover.
Controlled classification can be more accurate than unattended classification, but it relies heavily on representative sample locations, individual image processing skill, and spectral class excellence.

If two or more categories are very similar to each other in terms of their spectral reflection (e.g., seasonal grasslands, perennial grasslands or shallow and deep waters), the likelihood of misclassification will increase.

Controlled classification requires considerable attention to the development of representative samples. If representative samples are not expressive or unrepresentative, the rating results will also be poor, so the generally controlled classification requires more time and money than the unattended classification.

▸ Unattended classification:

1- It is based on fixed values in the program to classify areas or phenomena and is carried out through the Classification window we choose Iso Cluster Unsupervised Classification

Unattended classification

Our window shows where we choose the number of classification areas through Number of Classes and here we have 4 areas:

Unattended classification

Our final product appears as follows where the yellow color represents the urban distribution.

Unattended classification

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