science_cases:dlr_science_case
Differences
This shows you the differences between two versions of the page.
Next revision | Previous revision | ||
science_cases:dlr_science_case [2020/11/27 14:25] – created admin | science_cases:dlr_science_case [2022/10/20 14:25] (current) – admin | ||
---|---|---|---|
Line 3: | Line 3: | ||
===== Short description ===== | ===== Short description ===== | ||
- | Data returned from space missions has grown immensely in the last decade. Our goal is to understand the composition and evolution of planetary surfaces. We plan to extract the underlying information from this huge data set using unsupervsed | + | Data returned from space missions has grown immensely in the last decade. Our goal is to understand the composition and evolution of planetary surfaces. We plan to extract the underlying information from this huge data set using unsupervised |
===== Aim of the science case ===== | ===== Aim of the science case ===== | ||
On the one hand, we would like to have an assessment if current remote sensing data from NASA/ | On the one hand, we would like to have an assessment if current remote sensing data from NASA/ | ||
+ | |||
+ | ===== Long Description ===== | ||
+ | |||
+ | The sheer amount of data returned by scientific missions aimed at exploring the solar system and observing exoplanets in recent decades overwhelms classical methods to explore and discover important scientific aspects of the target body.\\ | ||
+ | As an example, the Mercury data return for Mariner 10 was less than 100 MB, while MESSENGER delivered about 23 TB. Future missions are expected to exceed this limit. In addition, there is a trend of increasing complexity in the data itself, e.g., going from the of Mariner-10 to the hyperspectral datasets expected from BepiColombo. This situation clearly indicates that some form of automated analysis would be beneficial, provided it is able to save time without a loss of the information content of the data. | ||
+ | |||
+ | Keeping the focus on hyperspectral remote sensing data, the typical approach for analysing this kind of data is to model the observed radiation with a forward radiative model (like Hapke, as in Hamilton et al., 2005) or attempt to reproduce the observed radiation by setting up relevant samples in a laboratory setting using chemical and/or geomorphological context information (e.g., Helbert et al., 2013). Complex forward models that are able to take into account the relevant physics are typically computationally intensive and difficult to use to investigate the very large parameter space covered by hyperspectral data. This consideration is even more relevant for laboratory investigations: | ||
+ | This process can suffer from user bias and typically only uses a few data points (e.g., 3-channel images) to describe different units. For example, Denevi et al. (2009) mapped the distribution and extent of major terrain types of Mercury using MESSENGER Mercury Dual Imaging System (MDIS) camera observations of Mercury. While the camera has 11 spectral bands, the maps typically used for the terrain differentiation are RGB, where 3 representative spectral bands are mapped onto the three image color channels. Geomorphological maps take in account additional features like surface roughness and crater density as a proxy for the age, where the correlation between age and crater density are derived from models (e.g., Bland, 2003; Kerr, 2006). | ||
+ | |||
+ | Automated techniques are becoming more common in planetary science applications, | ||
+ | |||
+ | **References: | ||
+ | |||
+ | * Bland, P. Crater counting. Astronomy & Geophysics, 44(4): | ||
+ | * Denevi, B. W., et al., MESSENGER global color observations: | ||
+ | * Hamilton et al., Mineralogy of Martian atmospheric dust inferred from thermal infrared spectra of aerosols. Journal of Geophysical Research, 110(E12), E12006, 2005. doi: 10.1029/ | ||
+ | * Helbert et al., Visible and near-infrared reflectance spectra of thermally processed synthetic sulfides as a potential analog for the hollow forming materials on Mercury. Earth and Planetary Science Letters, 369-370: | ||
+ | * Kerr, R. A. Who Can Read the Martian Clock? Science, 312(5777): | ||
+ | * McClintock, W. E. and Lankton, M. R. The mercury atmospheric and surface composition spectrometer for the MESSENGER mission. Space Science Reviews, 131(1-4): | ||
+ | |||
+ | ===== Description of the machine learning problem and our approach ===== | ||
+ | |||
+ | In this science case, Mercury surface reflectance data from the MASCS instrument onboard the NASA/ | ||
+ | |||
+ | First, NASA/PDS data is converted in a relational DB (PostgreSQL). Then the data is regridded with custom Postgis/ | ||
+ | |||
+ | Thus, data artifacts, instrumental and photometric residual effects are all removed. The resulting data cube has several hundred features that are compressed via blind signal demixing with **Independent Component Analysis** | ||
+ | |||
+ | Finally, the 2D maps were partitioned with **[[: | ||
+ | |||
+ | The spatial extent of the polar unit in the northern hemisphere generally correlates well with that of the northern volcanic plains and partially to the surface highest temperature models in the equatorial region. This may indicate an interaction between mineral composition and structure and surface temperature, | ||
+ | |||
+ | First results of the science case were presented at {{: | ||
science_cases/dlr_science_case.1606483539.txt.gz · Last modified: 2020/11/27 14:25 by admin