ID 原文 译文
24635 基于该思想，提出一种基于斑点统计特性保持的SAR影像迭代滤波算法。 Based on this idea, an iterative filtering method of SAR image based on speckle statistical characteristic preservation is proposed.
24636 该算法假设给定SAR影像的统计分布函数是先验已知的，即建模为混合Gamma分布，其分布参数可利用影像的像素值估计； The proposed method assumes that the statistical distribution function of a given SAR image is known a priori, namely the mixture Gamma distribution, and its distribution parameters can be estimated by the pixel values of the image.
24637 接着基于构建的混合Gamma分布模型，运用EM（Expectation Maximization）算法分割影像中的同质区域； Then, the EM (Expectation Maximization) algorithm is used to segment the SAR image based on the mixture Gamma distribution, so as to obtain the homogeneous regions in the image.
24638 再针对不同同质区域，选取拟合误差较大的灰度级，根据分割结果计算像素值为该灰度级的像素的密度，判断其是否是异常像素，并对异常像素运用 Frost 滤波器进行降噪。 Subsequently, for different homogeneous regions, select the gray levels with larger fitting errors, calculate the densities of the pixels whose values are the gray levels according to the segmentation results, and judge whether they are abnormal pixels, and the abnormal pixels are filtered by Frost filter.
24639 重复上述步骤，直到滤波后的影像直方图较好地服从统计分布函数。 Repeat above procedure until the histogram of filtered image fits the statistical distribution function well.
24640 GF-3 和 Radarsat-2 SAR 影像数据实验结果表明，该算法在保证影像质量的前提下，不仅能获得较好的统计建模结果，而且较好地抑制了相干斑噪声，实现影像降噪。 Experimental results of GF-3 and Radarsat-2 SAR image show that, on the premise of maintaining image quality, the proposed method can not only obtain better statistical modeling results, but also suppress speckle well and achieve image denoising.
24641 由于多智能体所处环境动态变化，并且单个智能体的决策也会影响其他智能体，这使得单智能体深度强化学习算法难以在多智能体环境中保持稳定。 Due to the dynamic change of multi-agent environment, and the decision of single agent will affect other agents, it is difficult for the deep reinforcement learning algorithm of single agent to maintain stability in multi-agent environment.
24642 为了适应多智能体环境，本文利用集中训练和分散执行框架Cen‑tralized Training with Decentralized Execution(CTDE)，对单智能体深度强化学习算法 Soft Actor‑Critic(SAC)进行了改进，引入智能体通信机制，构建 Multi‑Agent Soft Actor‑Critic(MASAC)算法。 In order to adapt to multi-agent environment, this paper uses centralized training and decentralized execution framework (CTDE) to improve single agent deep reinforcement learning algorithm soft actor-critic (SAC).
24643 MASAC 中智能体共享观察信息和历史经验，有效减少了环境不稳定性对算法造成的影响。 By introducing agent communication mechanism, in multi-agent soft actor-critic (MASAC), agents share observation information and historical experience, which effectively reduces the impact of environmental instability on the algorithm.
24644 最后，本文在协同以及协同竞争混合的任务中，对 MASAC算法性能进行了实验分析，结果表明MASAC相对于SAC在多智能体环境中具有更好的稳定性。 Finally, in the task of cooperation and cooperation and competition, the performance of MASAC algorithm is analyzed experimentally. The results show that MASAC has better stability than SAC in multi-agent environment.