# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 2 Materials and Methods
### RSIMeta: Metadata Management of Large-Scale Images with Heterogeneous Structures
#### 2.2.3 Multi-Source Heterogeneous Remote Sensing Images Integration  
As mentioned in Section 1, challenges of big remote sensing data include not only the large data volumes, but also the wide variety of data structures. In the past decades, hundreds of remote sensing satellites have been launched into spaces. Remote sensing data from these platforms are processed by different organizations and then described with different metadata schemas. Since the early 1990s, lots of national, regional, and international geospatial information metadata standards with different terminologies, structures, and purposes have been developed and applied to different remote sensing products [39; 40; 41; 42]. Ideally, large amounts of remote sensing data from different sources can complement each other to extract more comprehensive and accurate information about the earth. However, incompatible and isolated metadata standards make it hard to take full advantage of all these available data. Faced with these issues, ISO/TC211, a technical committee of the International Organization for Standardization (ISO), proposed ISO 19115 standard [43] for geospatial metadata in 2003, and then added metadata elements and schemas for imagery and gridded geospatial datasets in the ISO 19115-2 standard [44], which was published in 2009. Now the latest related standard about remote sensing image metadata is \"ISO 19115-2, Geographic Information-Metadata-Part 2: Extensions for acquisition and processing\" [45] published in 2019, which extends ISO19115-1(2014) and ISO/TS 19115-3(2016). The overall metadata structure of ISO 19115-2 is shown as Figure 14.  
ISO 19115 has been widely accepted for geospatial information metadata management and facilitate interoperability of remote sensing data from different sources. Taking Landsat as an example, the mapping from properties of a Landsat image to ISO 19115 metadata is shown as Figure 15.  
Figure 14: Overall metadata structure of ISO 19115-2. Figure 13: Problems of spatial query based on Hilbert grids. However, there still exist some different standards which have been adopted by other metadata systems, such as DIF 10, ECHO 10. To support multiple metadata standards continuously, NASA designed Unified Metadata Model (UMM) [46], which provided a cross-walk for mapping between CMR-supported metadata standards based on seven profiles: UMM-C, UMM-G, UMM-S, UMM-var, UMM-Vis, UMM-T, and UMM-Common. These profiles include metadata about datasets, images, end-to-end services, analysis and visualization tools, etc. Learning from the related experience and concepts of ISO 19115-2 and UMM, we designed a storage schema for remote sensing metadata with different data structures based on the distributed spatial index mentioned above. Just like \"Collection\" and \"Granule\" in ISO 19115-2 and UMM, we use \"Product\" and \"Metadata\" to store the information of the dataset and image separately. Product is used to store the description information about a dataset whose images are collected from the same source and processed with same methods. Metadata is used to store the detailed properties of the remote sensing image, and Metadata from different products may have different structures. Besides Product and Metadata, we designed another model, Image, which is used to store the common information of the images from different sources. Image is mainly used to improve the performance of large-scale heterogeneous remote sensing images retrieval. Considering that Product, Image, and Metadata are all structured models, we store their data in the relational database instead of XML files. The overall storage schema of RSIMeta is shown as Figure 16.  
In RSIMeta, every Product table has two associated tables: Image and Metadata. Metadata tables of different Products are distinct from each other, but the Image table is shared by all the Products. The distributed spatial index mentioned above is built on the Image table, which supports billions of image items efficient storage and retrieval.