Deep learning is a more sophisticated form of machine learning that enables a system to automatically discover representations in data. What differentiates deep learning from machine learning is its ability to continually improve a prediction on its own without external guidance or intervention. Deep learning algorithms learn patterns by progressing through a series of layers in a neural network to draw conclusions, similar to how the brain processes information.

For remote sensing, deep learning attempts to discover spatial and spectral representations in imagery. It is often used to find features such as vehicles, utility structures, roads, and other others. With ENVI Deep Learning, you can experiment with different parameters to achieve the best possible solution when training models.

Deep learning models are at the core of the overall process; they are defined by an underlying set of neural network parameters. You can create and train deep learning models in ENVI Deep Learning. Models can be shared with other users by publishing them to the ENVI Repository.

You can extract features with the following methods. The method to choose will depend on your intended use:

• Grid: Locate areas of interest in grids that contain one or more features.
• Object Detection: Locate groups of pixels that represent particular features.
• Pixel Segmentation: Classify pixels individually and assign them to a class label.

The Deep Learning Guide Map will guide you through the steps needed to extract features using these methods.