基于吉林一号遥感图像的星载目标快速识别系统

针对传统遥感图像地面目标识别系统图像获取周期长,信息实时性差等问题,设计星载目标快速识别系统,用于卫星在轨快速识别,提出改进的基于快速视网膜关键点(FREAK)的特征匹配识别算法,解决遥感图像数据量大、背景复杂的问题。介绍了星载目标快速识别系统的工作原理,提出简化的FREAK特征提取模型,将原有算法的七层模型减少为四层,用于快速提取出遥感图像中目标特征;利用二进制量化空间将高维特征数据量化为二维数据,提高算法的准确度;最后通过匹配,快速识别出遥感目标。实验结果表明,识别算法的准确度平均提高2.3%,识别用时缩短约27.8%,满足遥感卫星在轨目标快速识别的要求。

Target fast matching recognition of on-board system based on Jilin-1 satellite image

Aiming at problems such as long cycle and insufficient real time information in traditional remote sensing ground target image recognition system, an on-board target fast matching recognition platform is designed for fast on-orbit satellite recognition, and an improved feature matching recognition algorithm based on fast retinal key points (FREAK) is proposed to solve the problems of complex backgrounds and large amount of data in remote sensing image, First, we introduce the principle of on-board target recognition system and propose the simplified FREAK feature extraction model, and then we reduce the model of original algorithm from seven floors to four to quickly extract target features in remote sensing image. And then the high-dimensional feature data is quantified into two-dimensional data using binary quantization space, thus improving the accuracy of the algorithm; finally, the remote targets are recognized quickly by matching. The experimental results show that the matching accuracy can be increased by 2.3%, and matching time can be reduced by 27.8%. It can meet the requirements of quick identification of remote sensing satellite on-orbit targets.