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  • 1
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    Effect of Palagonite Dust Deposition on the Automated Detection of Carbonate Vis/NIR Spectra (2004)
    In:  CASI
    Details
    Publication Date: 2018-06-11
    Description: Currently Mars missions can collect more data than can be returned. Future rovers of increased mission lifetime will benefit from onboard autonomous data processing systems to guide the selection, measurement and return of scientifically important data. One approach is to train a neural net to recognize spectral reflectance characteristics of minerals of interest. We have developed a carbonate detector using a neural net algorithm trained on 10,000 synthetic Vis/NIR (350-2500 nm) spectra. The detector was able to correctly identify carbonates in the spectra of 30 carbonate and noncarbonate field samples with 100% success. However, Martian dust coatings strongly affect the spectral characteristics of surface rocks potentially masking the underlying substrate rock. In this experiment, we measure Vis/NIR spectra of calcite coated with different thicknesses of palagonite dust and evaluate the performance of the carbonate detector.
    Keywords: Instrumentation and Photography
    Type: Lunar and Planetary Science XXXV: Mars: New Methods and Techniques; LPI-Contrib-1197
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  • 2
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    Classification Experiments on Real-World Texture (2001)
    In:  Other Sources
    Details
    Publication Date: 2018-06-11
    Description: Many papers have been published concerning the analysis of visual texture and yet, very few application domains use texture for image classification. A possible reason for this low transfer of the technology is the lack of experience and testing in real-world imagery. In this paper, we assess the performance of texture-based classification methods on a number of real-world images relevant to autonomous navigation on cross-country terrain and to autonomous geology. Texture analysis will form part of the closed loop that allows a robotic system to navigate autonomously. We have implemented two different classifiers on features extracted by Gabor filter banks. The first classifier models feature distributions for each texture class using a mixture of Gaussians. Classification is performed using Maximum Likelihood. The second classifier represents local statistics using marginal histograms of the features over a region centered on the pixel to be classified. We measure system performance by comparison to ground truth image labels.
    Keywords: Instrumentation and Photography
    Type: Third Workshop on Empirical Evaluation Methods in Computer Vision, Kauai, Hawaii, December 10, 2001; Kauai, HI; United States
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  • 3
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    Smart Cameras for Remote Science Survey (2012)
    In:  Other Sources
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    Publication Date: 2019-07-13
    Description: Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels - distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.
    Keywords: Instrumentation and Photography
    Type: International Symposium on Artificial Intelligence Robotics and Automation in Space; Sep 04, 2012 - Sep 06, 2012; Turin; Italy
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  • 4
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    Metric Learning to Enhance Hyperspectral Image Segmentation (2013)
    In:  CASI
    Details
    Publication Date: 2019-07-12
    Description: Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.
    Keywords: Instrumentation and Photography
    Type: NPO-48092 , NASA Tech Briefs, January 2013; 36-37
    Format: application/pdf
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  • 5
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    ChemCam Automated Target Selection Status (2006)
    In:  Other Sources
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    Publication Date: 2019-07-13
    Description: Usage Scenarios: I) Identify candidate targets: a) Locate rocks; b) Select points on rocks. II) Identify and prioritize candidate targets: a) Locate rocks; b) Select points on rocks; c) Extract rock properties; and d) Prioritize points based on rock properties.
    Keywords: Instrumentation and Photography
    Type: Presentation to MSL ChemCam Team; Aug 01, 2006; Pasadena, CA
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  • 6
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    Metric Learning for Hyperspectral Image Segmentation (2011)
    In:  Other Sources
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    Publication Date: 2019-07-13
    Description: We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.
    Keywords: Instrumentation and Photography
    Type: IEEE Workshop on Hyperspectral Image Processing: Evolution in Remote Sensing; Jun 06, 2011 - Jun 09, 2011; Lisbon; Portugal
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  • 7
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    Using Trained Pixel Classifiers to Select Images of Interest (2004)
    In:  CASI
    Details
    Publication Date: 2019-08-15
    Description: We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.
    Keywords: Instrumentation and Photography
    Type: Interplanetary Network Progress Report; 42-158
    Format: application/pdf
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  • 8
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    Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering (2005)
    In:  CASI
    Details
    Publication Date: 2019-08-15
    Description: Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.
    Keywords: Instrumentation and Photography
    Type: IPN-PR-42-163
    Format: application/pdf
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  • 9
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    A Web-Based Search Service to Support Imaging Spectrometer Instrument Operations (2013)
    In:  Other Sources
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    Publication Date: 2019-07-13
    Description: Imaging spectrometers yield rich and informative data products, but interpreting them demands time and expertise. There is a continual need for new algorithms and methods for rapid first-draft analyses to assist analysts during instrument opera-tions. Intelligent data analyses can summarize scenes to draft geologic maps, searching images to direct op-erator attention to key features. This validates data quality while facilitating rapid tactical decision making to select followup targets. Ideally these algorithms would operate in seconds, never grow bored, and be free from observation bias about the kinds of mineral-ogy that will be found.
    Keywords: Instrumentation and Photography
    Type: Lunar and Planetary Science Conference; Mar 18, 2013 - Mar 22, 2013; The Woodlands, TX; United States
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  • 10
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    A Web-Based Search Service to Support Imaging Spectrometer Instrument Operations (2013)
    In:  Other Sources
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    Publication Date: 2019-07-13
    Description: No abstract available
    Keywords: Instrumentation and Photography
    Type: Lunar and Planetary Science Conference; Mar 18, 2013 - Mar 22, 2013; Woodlands, TX; United States
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