Sparse Superpixel Unmixing for Hyperspectral Image Analysis

Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals

Tracking the Martian CO2 Polar Ice Caps in Infrared Images

Researchers at NASA s Jet Propulsion Laboratory have developed a method for automatically tracking the polar caps on Mars as they advance and recede each year (see figure). The seasonal Mars polar caps are composed mainly of CO2 ice and are therefore cold enough to stand out clearly in infrared data collected by the Thermal Emission Imaging System (THEMIS) onboard the Mars Odyssey spacecraft. The Bimodal Image Temperature (BIT) histogram analysis algorithm analyzes raw, uncalibrated data to identify images that contain both "cold" ("polar cap") and "warm" ("not polar cap") pixels. The algorithm dynamically identifies the temperature that separates these two regions. This flexibility is critical, because in the absence of any calibration, the threshold temperature can vary significantly from image to image. Using the identified threshold, the algorithm classifies each pixel in the image as "polar cap" or "not polar cap," then identifies the image row that contains the spatial transition from "polar cap" to "not polar cap." While this method is useful for analyzing data that has already been returned by THEMIS, it has even more significance with respect to data that has not yet been collected. Instead of seeking the polar cap only in specific, targeted images, the simplicity and efficiency of this method makes it feasible for direct, onboard use. That is, THEMIS could continuously monitor its observations for any detections of the polar-cap edge, producing detections over a wide range of spatial and temporal conditions. This effort can greatly contribute to our understanding of long-term climatic change on Mars

Metric Learning to Enhance Hyperspectral Image Segmentation

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

Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery

Many current and future NASA missions are capable of collecting enormous amounts of data, of which only a small portion can be transmitted to Earth. Communications are limited due to distance, visibility constraints, and competing mission downlinks. Long missions and high-resolution, multispectral imaging devices easily produce data exceeding the available bandwidth. To address this situation computationally efficient algorithms were developed for analyzing science imagery onboard the spacecraft. These algorithms autonomously cluster the data into classes of similar imagery, enabling selective downlink of representatives of each class, and a map classifying the terrain imaged rather than the full dataset, reducing the volume of the downlinked data. A range of approaches was examined, including k-means clustering using image features based on color, texture, temporal, and spatial arrangemen

Centralized Alert-Processing and Asset Planning for Sensorwebs

A software program provides a Sensorweb architecture for alert-processing, event detection, asset allocation and planning, and visualization. It automatically tasks and re-tasks various types of assets such as satellites and robotic vehicles in response to alerts (fire, weather) extracted from various data sources, including low-level Webcam data. JPL has adapted cons iderable Sensorweb infrastructure that had been previously applied to NASA Earth Science applications. This NASA Earth Science Sensorweb has been in operational use since 2003, and has proven reliability of the Sensorweb technologies for robust event detection and autonomous response using space and ground assets. Unique features of the software include flexibility to a range of detection and tasking methods including those that require aggregation of data over spatial and temporal ranges, generality of the response structure to represent and implement a range of response campaigns, and the ability to respond rapidly

Towards Onboard Orbital Tracking of Seasonal Polar Volatiles on Mars

Current conditions on Mars support both a residual polar cap, composed mainly of water ice, and a seasonal cap, composed of CO2, which appears and disappears each winter. Kieffer and Titus characterized the recession of the seasonal south polar cap using an arctangent curve fit based on data from the Thermal Emission Spectrometer on Mars Global Surveyor [1]. They also found significant interannual deviations, at the regional scale, in the recession rate [2]. Further observations will enable the refinement of our models of polar cap evolution in both hemispheres. We have developed the Bimodal Image Temperature (BIT) Histogram Analysis method for the automated detection and tracking of the seasonal polar ice caps on Mars. It is specifically tailored for possible use onboard a spacecraft. We have evaluated BIT on uncalibrated data collected by the Thermal Emission Imaging System (THEMIS) instrument [3] on the Mars Odyssey spacecraft. In this paper, we focus on the northern seasonal cap, but our approach is directly applicable to the future analysis of the southern seasonal ice cap as well

An Autonomous Earth Observing Sensorweb

We describe a network of sensors linked by software and the internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the earth observing sensorweb application as well as overall architecture for the network

Adolescent Female Text Messaging Preferences to Prevent Pregnancy After an Emergency Department Visit: A Qualitative Analysis

Background: Over 15 million adolescents use the emergency department (ED) each year in the United States. Adolescent females who use the ED for medical care have been found to be at high risk for unintended pregnancy. Given that adolescents represent the largest users of text messaging and are receptive to receiving text messages related to their sexual health, the ED visit represents an opportunity for intervention. Objective: The aim of this qualitative study was to explore interest in and preferences for the content, frequency, and timing of an ED-based text message intervention to prevent pregnancy for adolescent females. Methods: We conducted semistructured, open-ended interviews in one urban ED in the United States with adolescent females aged 14-19 years. Eligible subjects were adolescents who were sexually active in the past 3 months, presented to the ED for a reproductive health complaint, owned a mobile phone, and did not use effective contraception. Using an interview guide, enrollment continued until saturation of key themes. The investigators designed sample text messages using the Health Beliefs Model and participants viewed these on a mobile phone. The team recorded, transcribed, and coded interviews based on thematic analysis using the qualitative analysis software NVivo and Excel. Results: Participants (n=14) were predominantly Hispanic (13/14; 93%), insured (13/14; 93%), ED users in the past year (12/14; 86%), and frequent text users (10/14; 71% had sent or received >30 texts per day). All were interested in receiving text messages from the ED about pregnancy prevention, favoring messages that were “brief,” “professional,” and “nonaccusatory.” Respondents favored texts with links to websites, repeated information regarding places to receive “confidential” care, and focused information on contraception options and misconceptions. Preferences for text message frequency varied from daily to monthly, with random hours of delivery to maintain “surprise.” No participant feared that text messages would violate her privacy. Conclusions: Adolescent female patients at high pregnancy risk are interested in ED-based pregnancy prevention provided by texting. Understanding preferences for the content, frequency, and timing of messages can guide in designing future interventions in the ED

Strategic Employee Development (SED) Program

As with many other U.S. agencies, succession planning is becoming a critical need for NASA. The primary drivers include (a) NASAs higher-than-average aged workforce with approximately 50 of employees eligible for retirement within 5 years; and (b) employees who need better developmental conversations to increase morale and retention. This problem is particularly concerning for Safety Mission Assurance (SMA) organizations since they traditionally rely on more experienced engineers and specialists to perform their organizations functions.In response to this challenge, the Kennedy Space Center (KSC) SMA organization created the Strategic Employee Development (SED) program. The SED programs goal is to provide a proactive method to counter the primary drivers by creating a deeper bench strength and providing a more comprehensive developmental feedback experience for the employee. The SED is a new succession planning framework that enables customization to any organization, and in this case, specifically for an SMA organization. This is accomplished via the identification of key positions, the corresponding critical competencies, and a process to help managers have relevant and meaningful development conversations with the workforce. As a result of the SED, several tools and products were created that allows management to make better strategic workforce decisions. Although there are opportunities for improvement for the SED program, the most important impact has been on the quality of developmental discussions for employees

The TechSat 21 Autonomous Sciencecraft Experiment

Software has been developed to perform a number of functions essential to autonomous operation in the Autonomous Sciencecraft Experiment (ASE), which is scheduled to be demonstrated aboard a constellation of three spacecraft, denoted TechSat 21, to be launched by the Air Force into orbit around the Earth in January 2006. A prior version of this software was reported in Software for an Autonomous Constellation of Satellites (NPO-30355), NASA Tech Briefs, Vol. 26, No. 11 (November 2002), page 44. The software includes the following components: Algorithms to analyze image data, generate scientific data products, and detect conditions, features, and events of potential scientific interest; A program that uses component-based computational models of hardware to analyze anomalous situations and to generate novel command sequences, including (when possible) commands to repair components diagnosed as faulty; A robust-execution-management component that uses the Spacecraft Command Language (SCL) software to enable event-driven processing and low-level autonomy; and The Continuous Activity Scheduling, Planning, Execution, and Replanning (CASPER) program for replanning activities, including downlink sessions, on the basis of scientific observations performed during previous orbit cycles