Performance of the Discrete Fourier Transform Satellite Imagery Classification Technique

摘要

The discrete Fourier transform (DFT) automated satellite imagery classification technique is designed to detect and identify cloud features from 25 x 25 nautical mile (nm) Defense Meteorological Satellite Program (DMSP) visible and infrared imagery samples. The DFT classifier performs two basic steps: feature extraction and sample identification. The feature extraction technique reduces and compresses the original visible and infrared data of an imagery sample in order to retain only the information needed to determine the cloud type of that sample. A multivariate normal density discriminant function then uses this information to identify the cloud type of the image sample from nine possible classes. The results obtained through this experiment show that the classifier can provide accurate and reliable cloud type classifications of satellite imagery samples. This report presents a detailed mathematical description of the DFT spectral classifier technique and offers some ideas for future modifications to the classifier. (Author)