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Target Detection Models

Overview​

This model identifies a specific class in an image. This is ideal for cases where a user wants to identify only one class with a high degree of confidence in an image.

Use Case​

Identify a specific material or object of interest within an image based on its unique spectral signature.

How It Works​

Target detection models sift through the entire hyperspectral dataset to find pixels or regions that match the spectral profile of the target material, allowing for precise detection of substances or items even in complex environments.

Training Requirements​

To train a target detection model, you will need a target class, as well as one or more non-targets. The non-targets can be any class in the image other than the target class.

Strengths​

  • Excel in accurately identifying and locating specific materials or objects within a scene
  • Use their distinct spectral signatures for precise detection
  • Particularly effective in scenarios where the target of interest occupies a small portion of the scene or is surrounded by a variety of other materials

When to Use​

Ideal when the goal is to find specific substances, objects, or features within a hyperspectral image, such as:

  • Detecting camouflaged vehicles in a forested area
  • Identifying minerals in a geological survey
  • Locating stressed vegetation

These models are best utilized when you have a well-defined target signature and need to isolate these targets from a complex background.