Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars

Abstract

Abstract:We propose to replace traditional spectral index methods by unsupervised spectral unmixing methods for the exploration of large datasets of planetary hyperspectral images. The main goal of this article is to test the ability of these analysis techniques to automatically extract the spectral signatures of the species present on the surface and to map their abundances accurately and with an acceptable processing time. We consider observations of the surface of Mars acquired by the imaging spectrometer OMEGA aboard MEX as a case study. The moderate spatial resolution (≈300 m/pixel at best) of this instrument implies the systematic existence of geographical mixtures possibly conjugated with non-linear (e.g., intimate) mixtures. We examine the sensitivity of a series of state-of-the-art methods of unmixing to the intrinsic spectral variability of the species in the image and to intimate assemblages of compounds. This study is made possible thanks to the use of well-controlled synthetic data and a real OMEGA image, for which the present icy species (water and carbon dioxide ices) and their characteristic spectra are widely known by the planetary community. Furthermore, reference maps of component abundances are built by the inversion of a more realistic physical model (simulating the propagation of solar light through the atmosphere and reflected back to the sensor) in order to validate the methods with the real image by comparison with the maps extracted by unmixing. The results produced by the processing pipeline of the eigenvalue likelihood maximization (ELM), vertex component analysis (VCA) and non-negativity condition least squares error estimators (NNLS) are the most robust to non-linear effects, highly-mixed pixels and different types of mixtures. Despite this fact, the produced results are not always the best because the VCA method assumes the existence of pure pixels in the image, that is pixels completely occupied by a single species. However, this pipeline is very fast and provides endmember spectra that are always interpretable. Finally, it produces more accurate distribution maps than the spectral index methods. More generally, the potential benefits of unsupervised spectral unmixing methods in planetary exploration is emphasized.

Can Terrestrial Restoration Methodologies be Transferred to Planetary Hyperspectral Imagery? A Quantitative Intercomparison and Discussion

“…Second, the CRISM and OMEGA datasets are taken as examples to test the capability and applicability of three types of advanced spatial-spectral hybrid methods, respectively. because of the lack of extraterrestrial ground truth, the accompanying infrared image obtained by the High-resolution Imaging Science Experiment (HiRISE) is resorted to validate the results in the real experiment of CRISM, whereas the reference spectra [23] are used to analyze the variation of endmember extraction accuracy quantitatively before and after denoising in the real scene of OMEGA. Finally, based on the restoration results, we discuss the spatial characteristics of melting frost on the Russell Dune via the intercomparison in CRISM case study and by exploring the component distribution on the south permanent cap through the experiments designed in the OMEGA case study.…”

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

“…Coarse spatial resolution is likely to increase the number of mixed pixels in aerial images, where the major proportion of the objects existing in a mixed pixel determine the identity of ground objects [39]. To some extent, spectral unmixing can handle the identity of mixed pixels, but it cannot provide spatial distribution information for remote sensing images [40].…”

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

“…Mixed pixels are common in remotely sensed hyperspectral images, due to the imaging spectrometer's insufficient spatial resolution as well as the intimate mixing effects [4][5][6]. Hyperspectral unmixing is a widely used technique to solve the mixed pixel problem according to analyzing the potential materials existing in each pixel and estimating the proportions of different materials [7,8]. Here, the materials in a mixed pixel are called endmembers, and the computed proportions are denoted as fractional abundances.…”