作　　者： (钮宇斌）; (王斌）;

机构地区： 复旦大学电磁波信息科学教育部重点实验室,上海200433 复旦大学信息学院智慧网络与系统研究中心,上海200433

出　　处： 《红外与毫米波学报》 2017年第4期471-480,共10页

摘　　要： 提出一种用于高光谱图像小目标探测的目标光谱学习算法,目的是从图像中学习得到一条更为准确的目标光谱,从而提高有监督目标探测的效果.该算法由基于自适应权重的目标光谱学习算法和自完备字典两部分组成.前一部分内容是在已有完备的背景字典的情况下,通过稀疏编码和梯度下降算法来优化学习目标光谱;后一部分内容通过背景字典的不断扩充来确保该字典的完备性,从而保证了学习算法的准确性.仿真和实际高光谱数据的实验结果表明,所提出的方法能有效地提取出准确的目标光谱,从而显著提高目标探测算法的精度. A novel target spectrum learning method for small target detection in hyperspectral imagery is proposed to obtain a more accurate target spectrum for better supervised target detection. This method is composed of two components： adaptive weighted learning method and self-completed background dictionary. Given a complete background dictionary, the former component refines the target spectrum through sparse coding and gradient descent algorithm. The latter component guarantees the background dictionary completeness by gradually size enlarging. Both experimental results on simulated and real hyperspectral data show that the proposed method has an advantage in extracting the accurate target spectrum, which enables better detection results.

关 键 词： 高光谱图像 目标探测 稀疏编码 光谱多样性 混合光谱