We present an automated method of generating high resolution (~ 2 meter) land cover using a pattern recognition neural network trained on spatial and spectral features obtained from over 9000 WorldView multispectral images (MSI) in six distinct world regions. At this resolution, the network can classify small-scale objects such as individual buildings, roads, and irrigation ponds. This paper focuses on three key areas. First, we describe our land cover generation process, which involves the co-registration and aggregation of multiple spatially overlapping MSI, post-aggregation processing, and the registration of land cover to OpenStreetMap (OSM) road vectors using feature correspondence. Second, we discuss the generation of land cover derivative products and their impact in the areas of region reduction and object detection. Finally, we discuss the process of globally scaling land cover generation using cloud computing via Amazon Web Services (AWS).
S. Craig Stutts, Benjamin L. Raskob, and Eric J. Wenger, "Globally scalable generation of high-resolution land cover from multispectral imagery," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 1019815 (Presented at SPIE Defense + Security: April 13, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2257436.
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