ABSTRACT
This chapter demonstrates the use of a scalable machine learning algorithm using airborne imagery data acquired by the National Agriculture Imagery Program (NAIP) for the Continental United States (CONUS) at an optimal spatial resolution of 1 m. It focuses on leveraging the power of deep belief networks (DBNs) for the representation and classification of aerial imagery data acquired at very high spatial resolutions of the order of 1 m. The chapter provides a detailed description of the unsupervised segmentation phase using the SRM algorithm. It enumerates the key components of the feature extraction step and the learning phase based on DBN. The chapter illustrates the details of the CRF algorithm and the online update procedure for the training data. It provides an overview of the NASA Earth Exchange high-performance computing (NEX HPC) architecture. The chapter contains results and comparative studies with National Land Cover Database (NLCD), and light detection and ranging (LiDAR). It also provides details of the AWS infrastructure.
