Compact Reconnaissance Imaging Spectrometer For Mars Images Research Articles

A novel algorithm for mapping carbonates using CRISM hyperspectral data

The algorithms for mapping carbonates from Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data use the depths of the diagnostic carbonate absorption features at ∼2.3 μm and ∼2.5 μm. However, because the band depths are estimated using fixed shoulder wavelengths, subtle shifts in band centres caused by different cations in the carbonates could result in false negatives for carbonates or false positives for other minerals that have absorption features in a similar wavelength range (eg. phyllosilicates, zeolites). This paper proposes a new algorithm that is based on the following attributes of carbonate spectra in the 2.0 to 3.0 μm range: (1) presence of two diagnostic overtones features around ∼2.3 μm and ∼2.5 μm; however, these features may show red shift or blue shift depending on the nature of cation(s); (2) the inter band gap between ∼2.3 μm and ∼2.5 μm carbonate absorption features, which remains relatively constant at ∼0.2 μm, even if there is a shift in the absorption features; (3) the contiguity of these two features, that is, carbonate spectra do not show any absorption features in between the above two features. The algorithm also includes a novel geometric continuum removal technique for locating the absorption features. The effectiveness of the algorithm is demonstrated using laboratory spectra, CRISM machine learning toolkit's mineral dataset, as well as CRISM images. The true positive rate (TPR), true negative rate (TNR) and overall accuracy for the method over the CRISM machine learning toolkit's mineral dataset are 29%, 87% and 83%, respectively.

A Fully-Automated Framework for Mineral Identification on Martian Surface Using Supervised Learning Models

The availability of various spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data on NASA PDS (Planetary Data System) hugely facilitated the research on the surface mineralogy of Mars, however, building supervised learning models for mineral mapping appears to be challenging due to the lack of ground-truth/training data. In this paper, an automated framework is presented that classifies the spectra in a CRISM hyperspectral image using supervised learning models, where the required training data is produced by augmenting the mineral spectra available in the MICA (Minerals Identified in CRISM Analysis) spectral library, that keeps the key absorption signatures in the mineral spectra intact while providing adequate variability. The framework contains a pre-processing pipeline that in addition to some conventional pre-processing steps includes a new feature extraction method to capture the information of the most distinguishable absorption patterns in the spectra. The proposed framework is validated on a set of CRISM images captured from different locations on the Martian surface by using different types of supervised learning models, like random forests, support vector machines, and neural networks. An uncertainty analysis of the different steps involved in the pre-processing pipeline is provided, as well as a comparison of performances with some of the previously used methods for this purpose, which shows this framework works comparably well with a mean accuracy of around 0.8. Interactive mineral maps are also provided for the detected dominant minerals.

The annual cycle of water vapor above gale crater as retrieved by CRISM and compared to ChemCam passive sky spectroscopy

Using spectra returned from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter (MRO), we report atmospheric water vapor column abundances at a spatial resolution of ~ 1 km from observations taken over Gale crater, the landing site of the Mars Science Laboratory (MSL). The observations span a little more than 3 Mars years (MY) from MY 28 at Ls = 116° (October 04, 2006) to MY 31 at Ls = 101° (April 24, 2012). The retrievals were performed using an improved algorithm to correct for the changes in wavelength in the cross-track direction of CRISM images, also known as the “spectral smile”, allowing for a larger fraction of area from each observation to be covered. The water vapor column abundances are retrieved using the strong absorption band of H2O centered at 2600 nm. The seasonal cycle of water vapor above Gale is compared against that from reprocessed and new MSL Curiosity rover ChemCam passive sky observations using updated gas absorption parameters and covering the period between MY 31 at Ls = 291° (March 30, 2013) and MY 35 at Ls = 42° (June 20, 2019). The retrieved annual variation of water vapor from CRISM and ChemCam both display a similar trend with respect to the timing of the maximum and minimum in water abundance, with only minor differences caused by the interannual differences in water vapor and the different retrieval approaches between both instruments, addressing a previously existing discrepancy between ChemCam and CRISM water vapor column abundances.

Dynamic aperture factor analysis/target transformation (DAFA/TT) for Mg-serpentine and Mg-carbonate mapping on Mars with CRISM near-infrared data

Serpentine and carbonate are products of serpentinization and carbonation processes on Earth, Mars, and other celestial bodies. Their presence implies that localized habitable environments may have existed on ancient Mars. Factor Analysis and Target Transformation (FATT) techniques have been applied to hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) to identify possible serpentine and Mg-carbonate-bearing outcrops. FATT techniques are capable of suggesting the presence of individual spectral signals in complex spectral mixtures. Applications of FATT techniques to CRISM data thus far only evaluate whether an entire analyzed image (≈ 3 × 105 pixels) may contain spectral information consistent with a specific mineral of interest. The spatial distribution of spectral signal from the possible mineral is not determined, making it difficult to validate a reported detection and also to understand the geologic context of any purported detections. We developed a method called Dynamic Aperture Factor Analysis/Target Transformation (DAFA/TT) to highlight the locations in a CRISM observation (or any similar laboratory or remotely acquired data set) most likely to contain spectra of specific minerals of interest. DAFA/TT determines the locations of possible target mineral spectral signals within hyperspectral images by performing FATT in small moving windows with different geometries, and only accepting pixels with positive detections in all cluster geometries as possible detections. DAFA/TT was applied to a hyperspectral image of a serpentinite from Oman for validation testing in a simplified laboratory setting. The mineral distribution determined by DAFA/TT application to the laboratory hyperspectral image was consistent with Raman analysis of the serpentinite sample. DAFA/TT also successfully mapped the spatial distribution of Mg-serpentine and Mg-carbonate previously detected in CRISM data using band parameter mapping and extraction of ratioed spectra. We applied DAFA/TT to CRISM images in some olivine-rich regions of Mars to characterize the spatial distribution of Mg-serpentine and Mg-carbonate-bearing outcrops.

Adversarial feature learning for improved mineral mapping of CRISM data

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven instrumental in the mineralogical analysis of the Martian surface. An essential tool for mineral identification for this dataset has been the CRISM summary parameters–which use simple mathematical functions to measure the presence/absence of specific spectral features. While the CRISM summary parameters and browse products (combinations of specific summary parameters) have proven valuable in guiding manual analysis, these hand-crafted representations are not well suited for automated analysis, as CRISM spectral artifacts and noise negatively affect their performance, making these parameters prone to false alarms. We propose an unsupervised technique based on Generative Adversarial Networks (GANs) to learn a more discriminative representation from CRISM data, such that simple metrics in the representation space are sufficient to discriminate between the various mineral spectra present in the data. We describe a simple pipeline using GAN based representations to map mineral signatures of interest across the CRISM image database. We show that the features learned by the GAN are better suited to discriminate mineral signatures in the CRISM database compared to the summary parameters and classical similarity metrics. Finally, we validate the technique over a subset of CRISM images over the Jezero crater and NE Syrtis regions of Mars.

A new method for atmospheric correction and de-noising of CRISM hyperspectral data

We propose a new method to perform atmospheric correction and de-noising on hyperspectral image cubes acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on board NASA’s Mars Reconnaissance Orbiter (MRO). The CRISM imager has had an important role in advancing our understanding of many aspects of Martian mineralogy. Many mineral detections from CRISM data have been facilitated by significant efforts in the development of the CRISM data processing pipeline to retrieve surface reflectance. However, some residuals remain in CRISM spectra after atmospheric correction, causing difficulty in the interpretation of processed reflectance spectra. In addition, CRISM images are occasionally corrupted with high noise levels exhibiting heterogeneous statistical properties. This paper identifies the cause of such spectral distortions and describe a technique that simultaneously performs both atmospheric correction and de-noising for each image cube individually. In particular, our method focuses on the 1.0–2.6 μm wavelength region of CRISM images and is applicable to images of non-icy surfaces. Experimental results show that our technique is able to significantly mitigate noise and distortions from various sources like gaseous absorptions, detector temperature, and water ice aerosols, compared with the atmospheric correction method in the CRISM official processing pipeline called volcano scan correction, for a variety of scenes. Careful validations that include the qualitative examination of noise and artifacts both on ratioed and non-ratioed spectra and comparison using multiple overlapping images strengthen confidence in our approach.

Mineralogic evidence for subglacial volcanism in the Sisyphi Montes region of Mars

Here we examine the mineral assemblages detected on possible glaciovolcanic edifices in the Sisyphi Planum region of Mars, a high-latitude region in the southern highlands nestled between the Argyre and Hellas impact basins. Minerals were identified utilizing visible/near-infrared spectra from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). Analysis of eleven CRISM images located on the volcanic edifices revealed three distinct spectral classes in the region which are interpreted to be: gypsum-dominated, smectite–zeolite–iron oxide-dominated (possibly palagonite), and polyhydrated sulfate-dominated material. While sulfates can form under a variety of alteration conditions, palagonite-like mineral assemblages require low-temperature and high water-to-rock hydrothermal conditions typically found in subglacial or subaqueous volcanic eruptions. The possible palagonite detections on the volcanic edifices, the geomorphology of the region, and the analogous terrestrial mineralogy of subglacial eruptions strongly suggests the formation of these minerals during subglacial eruptions or associated hydrothermal systems. This implies that thick water ice sheets were present in this region in the late Noachian or early Hesperian, and that the subglacial hydrothermal systems could have supported habitable environments with excellent biosignature preservation potential.

A search for minerals associated with serpentinization across Mars using CRISM spectral data

Sites associated with serpentinization processes, both on Earth and throughout the Solar System, are becoming increasingly compelling for the study of habitability and astrobiology. The co-occurrence of serpentine, Mg-carbonate, and talc/saponite on Mars is most like terrestrial sites where this mineral suite is produced in low-temperature serpentinizing environments, and where on Earth these reactions support biological activity. This study aims to understand the global distribution of minerals associated with serpentinization. We performed a comprehensive analysis of the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) spectral dataset using factor analysis and target transformation methods to efficiently parse through the large quantity of data. These methods allow for the rapid analysis of thousands of images and provide a quantitative means to determine the significant spectral constituents of an image. These methods were used to produce a global distribution map of CRISM images with a significant likelihood of containing the spectral types of interest. Previous detections of serpentine using traditional CRISM analysis techniques were typically corroborated and additional detections were identified in isolated locations across the martian southern highlands. Most serpentine across Mars is associated with another Fe/Mg-phyllosilicate phase like talc and/or saponite. Except for in the Nili Fossae region, serpentine shows no clear relationship with ultramafic bedrock or with the other mineral phases investigated (Mg-carbonate and talc/saponite). Most serpentine detections were found in isolated exposures, associated with crater ejecta, knobby terrain, or as part of discontinuous layers in crater or valley walls. Nili Fossae shows more pervasive and extensive detections of a serpentine + phyllosilicate endmember than previously recognized, particularly in the eastern portion of Nili Fossae where the highest concentration of olivine-rich basalts is located. These findings imply that large, regional-scale near surface serpentinizing systems were likely rare on Mars. However, low-concentration serpentine detections across the southern highlands do suggest more pervasive serpentinization early in Mars history, when the planet was more geologically active.

Global analysis of gully composition using manual and automated exploration of CRISM imagery

Gully formations on Mars have been the focus of many morphological and mineralogical studies aimed at inferring the mechanisms of their formation and evolution. In this paper we have analyzed 354 globally distributed gully-bearing Full Resolution Targeted (FRT) Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) images. The primary goal of the analysis was to identify all spectrally distinct deposits in these images (if any) and to classify them into hydrated and non-hydrated categories using only CRISM summary parameters (Viviano-Beck et al., 2014). Such approach makes possible to analyze a very large set of all distinct deposits in 354 images. We found that 68% of these images lack any distinct deposits, 8% of images contain non-hydrated deposits which coincide with the gullies and 24% of images contain hydrated deposits which coincide with the gullies. These results are compared with the recent analysis of 110 CRISM images by Nuñez et al. (2016) who also found that most gullies coincide with indistinct deposits, but, contrary to our findings, they found a predominance of non-hydrated minerals among distinct deposits. We attribute this discrepancy in part to their smaller and geographically biased sample of images, and in part to differing protocols of categorizing images. The discrepancy between the two surveys is further increased if we count all deposits in FRT gully-bearing images, not just deposits directly coinciding with the gullies, obtaining 44% indistinct, 15% non-hydrated, and 41% hydrated images. The secondary goal of this study was to perform the same image survey using a recently developed automated method in order to assess its accuracy and thus its feasibility for performing future surveys. We found the overall accuracy of the auto-mapper to be 76.2% but its accuracy for discovering distinct deposits, and in particular, distinct hydrated deposits was lower. We attributed the deficiencies of the auto-mapper primarily to its sensitivity to presence of noise in images and especially to presence of speckle noise. It is however worth noting that qualitatively both manual and automatic surveys arrived at the same overall conclusion.

Identification and refinement of martian surface mineralogy using factor analysis and target transformation of near-infrared spectroscopic data

Factor analysis and target transformation techniques were applied to the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) spectral dataset to identify spectral endmembers, reduce noise, and automate image analysis. These techniques allow for rapid processing of images and identification of weak spectral signals. We have applied the automated technique to over 3000 CRISM images and successfully identified endmembers including phyllosilicates (e.g., serpentine, nontronite, and illite), sulfates (e.g., gypsum), carbonates (e.g., magnesite) and hydrated silica. To test these techniques, factor analysis and target transformation were applied to all available full spectral resolution covering the Nili Fossae region from 1.7 to 2. 6µm data to identify the occurrence of Mg-carbonate in the region. We have also applied the factor analysis and target transformation as a noise reduction algorithm, which also allows for improved results from other common image analysis techniques, including spectral ratios and index maps.

Automatic, exploratory mineralogical mapping of CRISM imagery using summary product signatures

Martian spectroscopic and mineralogical analysis is usually performed using Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) browse products – false color images which show the spatial distribution of absorption features at key wavelengths. This manual, time-consuming method is ill-suited for exploratory surveys of a large number of images – for such surveys an automatic methodology is needed. In this paper we propose a method for exploratory but fully automatic mineralogical mapping of CRISM images. In our approach pixels are characterized by vectors of CRISM summary product values instead of spectral functions, and mineralogical units are discovered using a clustering principle. Moreover, the rare class discovery algorithm DEMUD is used in place of a standard clustering algorithm to identify mineralogical units - enabling the identification of only scientifically interesting, possibly rare, mineralogical deposits. The method outputs a map for each site showing the spatial distribution of mineralogical units – areas characterized by similar mineralogy. It also provides, without using a spectral library, semantic labels for each unit. We envision our method as a focus-of-attention tool to facilitate fast exploratory surveys of a large number of images. An analyst needs only to examine manually regions within an image where our pipeline indicates the existence of mineral units of interest. In this paper the method for our computational pipeline is described in detail and its performance is evaluated using a sample of 20 CRISM images – the mineralogical content of which is known from manual analysis. We find that our pipeline identifies most deposits found through manual analysis as well as some additional deposits which were not targeted by those analyses. Overall, we conclude that our fully automatic mineralogical mapper works well for exploratory purposes. Thus, it adds a new, valuable functionality to existing tools for CRISM imagery analysis.

Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study

As the volume of hyperspectral data for planetary exploration increases, efficient yet accurate algorithms are decisive for their analysis. In this paper, the capability of spectral unmixing for analyzing hyperspectral images from Mars is investigated. For that purpose, we consider the Russell megadune observed by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the High-Resolution Imaging Science Experiment (HiRISE) instruments. In late winter, this area of Mars is appropriate for testing linear unmixing techniques because of the geographical coexistence of seasonal CO2 ice and defrosting dusty features that is not resolved by CRISM. Linear unmixing is carried out on a selected CRISM image by seven state-of-the-art approaches based on different principles. Three physically coherent sources with an increasing fingerprint of dust are recognized by the majority of the methods. Processing of HiRISE imagery allows the construction of a ground truth in the form of a reference abundance map related to the defrosting features. Validation of abundances estimated by spectral unmixing is carried out in an independent and quantitative manner by comparison with the ground truth. The quality of the results is estimated through the correlation coefficient and average error between the reconstructed and reference abundance maps. Intercomparison of the selected linear unmixing approaches is performed. Global and local comparisons show that misregistration inaccuracies between the HiRISE and CRISM images represent the major source of error. We also conclude that abundance maps provided by three methods out of seven are generally accurate, i.e., sufficient for a planetary interpretation.

Spectral Smile Correction of CRISM/MRO Hyperspectral Images

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is affected by a common artifact to pushbroom-type imaging spectrometers, the so-called “spectral smile.” For this reason, the central wavelength and the width of the instrument spectral response vary according to the spatial dimension of the detector array. As a result, the spectral capabilities of CRISM get deteriorated for the off-axis detector elements while the distortions are minimal in the center of the detector array, the so-called “sweet spot.” The smile effect results in a data bias that affects hyperspectral images and whose magnitude depends on the column position (i.e., the spatial position of the corresponding detector element) and the local shape of the observed spectrum. The latter is singularly critical for images that contain chemical components having strong absorption bands, such as carbon dioxide on Mars in the gas or solid phase. The smile correction of CRISM hyperspectral images is addressed by the definition of a two-step method that aims at mimicking a smile-free spectral response for all data columns. First, the central wavelength is uniformed by resampling all spectra to the sweet-spot wavelengths. Second, the nonuniform width of the spectral response is overcome by using a spectral sharpening which aims at mimicking an increase of the spectral resolution. In this step, only spectral channels particularly suffering from the smile effect are processed. The smile correction of two CRISM images by the proposed method show remarkable results regarding the correction of the artifact effects and the preservation of the original spectra.

Hydrothermal formation of Clay-Carbonate alteration assemblages in the Nili Fossae region of Mars

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has returned observations of the Nili Fossae region indicating the presence of Mg-carbonate in small (< 10 km sq 2), relatively bright rock units that are commonly fractured (Ehlmann et al., 2008b). We have analyzed spectra from CRISM images and used co-located HiRISE images in order to further characterize these carbonate-bearing units. We applied absorption band mapping techniques to investigate a range of possible phyllosilicate and carbonate minerals that could be present in the Nili Fossae region. We also describe a clay-carbonate hydrothermal alteration mineral assemblage in the Archean Warrawoona Group of Western Australia that is a potential Earth analog to the Nili Fossae carbonate-bearing rock units. We discuss the geological and biological implications for hydrothermal processes on Noachian Mars.