The Gamma Generalized Normal Distribution: A Descriptor of SAR Imagery
This work addresses the need for better statistical models in remote sensing for SAR imagery analysis, but it appears incremental as it builds on an existing distribution.
The authors tackled the problem of modeling synthetic aperture radar (SAR) imagery by proposing a new four-parameter distribution called the gamma generalized normal (GGN), which combines gamma and generalized normal distributions, and found that it can outperform the existing beta generalized normal distribution in some contexts.
We propose a new four-parameter distribution for modeling synthetic aperture radar (SAR) imagery named the gamma generalized normal (GGN) by combining the gamma and generalized normal distributions. A mathematical characterization of the new distribution is provided by identifying the limit behavior and by calculating the density and moment expansions. The GGN model performance is evaluated on both synthetic and actual data and, for that, maximum likelihood estimation and random number generation are discussed. The proposed distribution is compared with the beta generalized normal distribution (BGN), which has already shown to appropriately represent SAR imagery. The performance of these two distributions are measured by means of statistics which provide evidence that the GGN can outperform the BGN distribution in some contexts.