# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis
### LSI Model-Based Metadata Retrieval Experiment  
In addition, in order to verify the retrieval efficiency for massive and multi-source remote sensing data, we also simulated about 15 million remote sensing metadata files. All of them are organized by the LSI model and imported into our metadata index and retrieval system SolrCloud. The total amount of our experimental metadata is shown in Table 5.  
In order to test the retrieval capabilities for different volumes of big data, the 15 million pieces of remote sensing metadata were copied and divided into six groups, and the volumes of the groups were 1 million, 3 million, 5.5 million, 7.5 million, 10 million, and 15 million. For the follow-up experiments, each group contained only 896,981 Landsat 8 OLI_TIRS pieces of metadata. In all the following experiments, we always set the platform and sensor parameters to Landsat 8 and OLI_TIRS, respectively, with only spatial and time parameters changing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Data Type** & \begin{tabular}{c} **Volume of Images** \\ **Stored in Sub-Center** \\ \end{tabular} & \
\begin{tabular}{c} **Volume of Images** \\ **Integrated by Main Center** \\ \end{tabular} &
\begin{tabular}{c} **Average Transfer Rate** \\ **(MB/s)** \\ \end{tabular} \\ \hline Landsat 8 & OLI\_TIRS & 310 & 310 & 9.8 \\ HJ-1A & HSI & 350 & 350 & 10.1 \\ CEBERS-1/2 & CCD & 270 & 11.7 \\ Landsat 7 & ETM+ & 450 & 450 & 10.5 \\ Landsat-5 & MSS & 260 & 260 & 12.8 \\ HJ-1A/B & CCD & 710 & 710 & 9.9 \\ Landsat 5 & TM & 430 & 430 & 13.8 \\ FY-3A/B & VIRR & 450 & 450 & 11.2 \\ Aster & L1T & 150 & 150 & 10.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of distributed data integration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sub-Center** & **Satellite** & **Data Type** & **Volume of Images** & **Image Format** \\ \hline
1 & Landsat 8 & OLI\_TIRS & 310 & GeoTIFF \\
2 & HJ-1A & HSI & 350 & HDF5 \\
2 & CEBERS-1/2 & CCD & 270 & GeoTIFF \\
3 & Landsat 7 & ETM+ & 450 & GeoTIFF \\
4 & Landsat1-5 & MSS & 260 & GeoTIFF \\
5 & HJ-1A/B & CCD & 710 & GeoTIFF \\
6 & Landsat 5 & TM & 430 & GeoTIFF \\
7 & FY-3A/B & VIRR & 450 & HDF5 \\
8 & Aster & L1T & 150 & HDF4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A Summary of the Experimental Images. As for the spatial query parameters, there were several: parameters within 1 GeoSOT grid, 2 GeoSOT grids, and 4 GeoSOT grids. Therefore, the retrieval experiment of each group was divided into three subgroups. As for the time query parameters, in each subgroup of experiments, the query time frames were set to one day, one month, and six months, in order to verify the performance of our proposed method thoroughly. Furthermore, in order to exclude the influence of accidental factors, 20 retrievals were executed separately in each experiment and the average query time was the final result. In addition, taking the GeoSOT code of the center point as the spatial identification of each remote sensing data is equivalent to converting polygon queries to point queries. It will improve the efficiency of data query, but the precision will be discounted. Therefore, a second filtering process, using longitude and latitude, should be made for the query results. Since the first query had ruled out the vast majority of irrelevant data, the second filtering process took little time. Hence, the efficiency of LSI model-based data query was very high. The search conditions of each group of experiment and time consumed are as shown in Table 6.