NOAA极轨气象卫星数据格式详解-AVHRR图像信息

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"图像数据块信息说明 - facts and fallacies of software engineering" 本文主要涉及的是NOAA极轨气象卫星的数据格式,特别是AVHRR(Advanced Very High Resolution Radiometer,高级甚高分辨率辐射计)数据的组织方式。AVHRR是NOAA系列卫星上的关键传感器,用于收集地球的气象和环境数据。 在NOAA系列极轨气象卫星中,例如NOAA-9到NOAA-14以及NOAA-15到NOAA-17,它们都采用了三轴稳定的卫星姿态,具有较高的扫描率(6条扫描线/秒),对地扫描角度为±55.4度,星下点分辨率大约为1.1公里。这些卫星在太阳同步轨道上运行,高度约为800-850公里,轨道倾角在98.6-99.1度之间,周期大约101-102分钟,每天环绕地球14次,回归周期约9天。 AVHRR数据的存储和记录方式是关键。每个象元点包含5个波段的信息,每个波段的取样值是10位(10bit)。每3个取样值(共30bit)被编码到4个字节(32bit)中。这4个字节的前2位是空位,接着是按照波段1到5的顺序记录的取样值。最后一个字节仅存放一个取样值,其余20位填充为零。这种数据压缩和存储方法有助于减少存储需求,同时保持数据的完整性。 对于不同代的NOAA卫星,数据格式也有所不同。例如,第五代的NOAA-15到NOAA-17卫星上,AVHRR探测器升级为AVHRR/3,增加了CH3A通道,并且TOVS(Total Ozone Mapping Spectrometer,总臭氧测绘光谱仪)升级为ATOVS(Advanced TOVS,先进的总臭氧测绘光谱仪),还添加了微波探测器等先进技术。1B数据格式也发生了变化,从压缩形式转变为二进制长字节文件,使得数据处理和分析更为高效。 具体到NOAA-1B数据格式,有以下几点: 1. 压缩形式的1B格式:早期的NOAA卫星数据可能以压缩的形式存在,需要解压缩后才能处理。 2. NOAA_K/L/M/N(15,16,17..)卫星1B数据格式:这是第五代NOAA卫星的数据格式,它包含了更多的信息和改进的传感器数据。 3. NOAA-16/17 ATOVS LEVEL1数据文件格式:这些卫星的1B数据文件进一步详细描述了观测到的地球表面特征,包括大气和地表参数。 这些信息对于理解NOAA卫星数据的结构和处理是至关重要的,无论是为了气象预报、气候研究还是环境监测,都需要对这种数据格式有深入的理解。通过解析和处理这些数据,科学家和研究人员可以获取到全球范围内的气候和环境变化的详细信息。

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

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