2025 Big Sky GeoCon - Mapping the Future: Innovations in Geospatial Technology

Local raster texture is an important attribute that can be used for sample design and raster-based classification, regression, and clustering. However, determining the appropriate kernel weights to quantify texture can be challenging and can vary depending on the remote sensing or computer vision task. We present an automated approach that uses principal component analysis (PCA) and convolution kernels to quantify orthogonal intensity and texture metrics. Our approach linearly projects raster data across covariance axes, learns kernel cell weights, and provides a straightforward mechanism to retain as much information as possible within the raster dataset while removing redundancy.