Binary Classification

What is Binary Classification?

Binary classification in the context of spatial analysis involves categorizing the cells of a raster dataset into two distinct classes or groups. This process is essential for simplifying complex raster data by reducing it into a binary format. Each pixel in the raster is assigned a value of either 0 or 1, representing the two classes. This type of classification is often used to separate areas of interest from a background, such as distinguishing water bodies from land or classifying areas as urban or non-urban.

When would you use Binary Classification?

You would use binary classification when there is a need to simplify raster data into two distinct categories for analysis. It is particularly useful in situations where decision-making is based on the presence or absence of certain features. Examples include:

Identifying flood-affected areas in satellite imagery.
Demarcating vegetation cover versus non-vegetated regions.
Separating built-up and non-built-up areas in urban planning studies.
Detecting changes in land use by classifying areas pre and post-change event.

FAQs

How do I choose the criteria for classification?

Select criteria based on the specific features or attributes that need to be distinguished in your analysis. These criteria can be thresholds set on pixel values, such as spectral reflectance values corresponding to certain land cover types.

What data input do I need for a binary classification?

You need a raster dataset that includes quantitative data which can be split into two legitimate categories based on your specified criteria.

Can binary classification be applied to multi-band images?

Yes, binary classification can be performed on multi-band images by examining each band separately or through indices derived from multiple bands, such as the Normalized Difference Vegetation Index (NDVI) for vegetation classification.

Is preprocessing required before running binary classification?

Preprocessing steps such as radiometric correction, geometric correction, or noise filtering may be necessary to enhance the accuracy of the classification output.