论文标题
伽马广义的正态分布：SAR图像的描述符
The Gamma Generalized Normal Distribution: A Descriptor of SAR Imagery
论文作者
论文摘要
我们提出了一种新的四参数分布，用于建模合成孔径雷达（SAR）图像，通过结合伽玛和广义正常分布，称为伽马广义正常（GGN）。通过识别极限行为和计算密度和力矩扩展，提供了新分布的数学表征。在合成数据和实际数据上都评估了GGN模型性能，并讨论了最大似然估计和随机数的生成。将所提出的分布与Beta广义正态分布（BGN）进行了比较，该分布已经证明适当地代表了SAR图像。这两个分布的性能是通过统计数据来衡量的，这些统计数据提供了GGN在某些情况下可以优于BGN分布的证据。
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.
