Textural Classification of Clouds In Satellite Imagery Using Geometric Measures

Visidyne proposes to develop an expert, neural net based, system for classifying cloud types based on texture analysis in satellite imagery using geometric measures as network inputs. In Phase I Visidyne proposes to investigate the utility of a variety of network inputs based on the geometry and characteristic spatial scales of cloud texture in satellite imagery. These measures will be compared with the more traditional statistical ones for a sampling of different cloud types in NOAA satellite images. If a set of geometric measures with good correlation to visually typed cloud images is found, then development of a neural net based expert system for classifying cloud types will be proposed for Phase II. The primary benefit of Phase I will be the evaluation of the utility of geometric as opposed to statistical measures for use in cloud typing based on satellite image texture analysis. If Phase I is successful then candidate sets of geometric measures will have been identified for evaluation as inputs for a neural network based expert system for cloud typing in satellite imagery.