基于蒸馏响应的类增量少样本遥感目标检测研究

近年来,少样本遥感目标检测越来越受到人们的关注,其目的是使目标检测器仅用少量标记的遥感图像样本就能检测到新的目标类别。但主流的少样本遥感目标检测器在学习新类知识的时候,必需要有基类样本的辅助。而在现实世界中,目标类别往往是不断变化的,且环境也在不断变换。这种目标动态性给传统的少样本目标检测方法带来了巨大的障碍,因为它们严重依赖基类样本来学习新类别,这就使这些少样本遥感目标检测器无法无限制的对新类别进行学习,而且随着学习的类别数不断增多,在内存使用量、存储用量和计算量上都将产生严重负担。本研究从实际角度出发,提出了基于蒸馏响应的类增量遥感少样本目标检测算法(Class-incremental Few-Shot Object Detection with Distillation Response inremote sensing images,DR-CFSOD),以实现在不需要基类数据辅助的情况下,利用新类数据独自完成学习任务。此外,本文所研究的算法为了缓解增量学习场景中经常发生的灾难性遗忘问题,通过提取从以前的类中学习到的知识,保留了模型的基类判别能力。为了验证所提方法的有效性,在两个不同的遥感图像数据集上进行了广泛的实验,结果表明,DR-CFSOD比基线方法具有更好的性能。

Optimization of Molecular Distillation for Removal of Limonene from Ponkan Essential Oil Using Response Surface Methodology

Ponkan (Citrus reticulate Blanco) is one of the most popular citrus fruits for daily consumption and processing in China. In this study, limonene was removed from ponkan essential oil (PEO) by using an effective methodology to estimate the optimum levels of three independent variables of molecular distillation (MD) in order to minimize the limonene content and (b) maximize the removal rate. A three-factor Box-Behnken design (BBD) was employed to evaluate the effect of distilling temperature (45.00~55.00 ℃), wiper rolling speed (150.00~250.00 r/ min) and distilling pressure (25.00~35.00 Pa). The optimum parameters within the experimental range of the variables were extrapolated to be: distilling temperature 47.00℃, wiper rolling speed 203.00 r/min and distilling pressure 30.00 Pa. Optimal values extrapolated by response surface methodology (RSM) for the limonene content and removal rate of the desirable PEO were 47.82% and 84.67%, respectively.