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 in order 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 others. With ENVI Deep Learning, you can experiment with different parameters to achieve the best possible solution when training models.
TensorFlow models are at the core of the overall process. TensorFlow is an open-source library that ENVI uses to perform deep learning tasks. A TensorFlow model is defined by an underlying set of neural network parameters.
ENVI Deep Learning provides two methods for extracting features, depending on your intended use:
Pixel Segmentation: Classify pixels individually.
Object Detection: Locate groups of pixels that represent particular features.
Grid: Locate areas of interest that contain one or more features.
The Deep Learning Guide Map will guide you through the steps needed to extract features using these methods.