ID	原文	译文
54017	准则表明，SULA阵元间距和旋转角度满足特定关系时，其转动前后的模糊角在同一坐标系下不互相重合。	The theorem reveals that when the element spacing and rotatory angle satisfy certain constraint, the ambiguities before and after the rotation will not overlap in the same coordinate system.
54018	基于该准则，本文进一步提出多重联合MUSIC(MJ-MUSIC)解模糊方法。该方法使用稀疏X形阵列的夹角代替了SULA的旋转，使虚假峰相互交错，	Based on the theorem, a MJ-MUSIC(Multiple-Joint MUSIC) method was further proposed for applying on a sparse X-shaped array, which replaces the SULA rotation with its included angle and staggers the ambiguities.
54019	并通过联合X形阵两臂上的接收信号，进一步增加真实峰与虚假峰之间的差值，提高了DOA解模糊的正确率，	By combining the received signals from both arms of the array, the difference between actual peaks and spurious ones can be amplified, which leads to better estimation correct rate.
54020	仿真实验验证了旋转模糊对消准则与MJ-MUSIC方法的正确性和有效性。	Simulation results demonstrate that the correctness and effectiveness of proposed theorem and MJ-MUSIC method.
54021	针对训练样本量少导致高光谱图像分类精度低的问题，本文提出了一种基于字典优化的联合稀疏表示高光谱图像分类方法。	Aiming at the problem of low training sample size leading to low classification accuracy of hyperspectral images, this paper proposes a joint sparse representation hyperspectral image classification method based on dictionary optimization.
54022	首先，采取基于层次聚类的波段选择方法降低高光谱图像数据维度;	First, the band selection method based on hierarchical clustering is adopted to reduce the dimensionality of hyperspectral image data;
54023	其次，结合空间信息将高光谱数据划分为多个子集，利用已知标签信息的训练样本标记各个子集中可能成为训练样本的像元，	second, the hyperspectral data is divided into multiple subsets based on spatial information, and training samples with known label information are used to mark each subset that may become training samples.
54024	组成训练样本备选集，根据光谱相似度准则筛选备选集得到优化字典;	Pixels form a candidate set of training samples, and the candidate set is filtered according to the spectral similarity criterion to obtain an optimized dictionary;
54025	最后，将优化字典用于联合稀疏表示对高光谱图像进行分类。	finally, the optimized dictionary is used to classify hyperspectral images through joint sparse representation.
54026	通过Indian Pines数据集和Pavia University数据集仿真实验表明，本文提出的分类算法能够有效提高高光谱图像分类精度。	The simulation experiments of Indian Pines dataset and Pavia University dataset show that the classification algorithm proposed in this paper can effectively improve the classification accuracy of hyperspectral images.