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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Unsupervised classification of MSS Landsat data for mapping spatially complex vegetation

The various stages in carrying out a monocluster block unsupervised classification using Landsat MSS data are described. Procedures for carrying out these various stages were found to be far from well-established for the type of terrain being investigated, which is rugged and contains many small land cover units. Two particular difficulties were encountered: first, that of precise ground location of pixels; and, secondly, that of objectively evaluating the results. Ways in which these can be surmounted are suggested.

Townshend, J. R. G.↗
NASA NTRS1989

Unsupervised classification of scattering behavior using radar polarimetry data

The use of an imaging radar polarimeter data for unsupervised classification of scattering behavior is described by comparing the polarization properties of each pixel in a image to that of simple classes of scattering such as even number of reflections, odd number of reflections, and diffuse scattering. For example, when this algorithm is applied to data acquired over the San Francisco Bay area in California, it classifies scattering by the ocean as being similar to that predicted by the class of odd number of reflections, scattering by the urban area as being similar to that predicted by the class of even number of reflections, and scattering by the Golden Gate Park as being similar to that predicted by the diffuse scattering class. It also classifies the scattering by a lighthouse in the ocean and boats on the ocean surface as being similar to that predicted by the even number of reflections class, making it easy to identify these objects against the background of the surrounding ocean. The algorithm is also applied to forested areas and shows that scattering from clear-cut areas and agricultural fields is mostly similar to that predicted by the odd number of reflections class, while the scattering from tree-covered areas generally is classified as being a mixture of pixels exhibiting the characteristics of all three classes, although each pixel is identified with only a single class.

Van Zyl, Jakob J.↗
NASA NTRS1989

Unsupervised learning in neurodynamics using example-interaction approach

A new concept for unsupervised learning based upon examples introduced to the neural network is proposed. Each example is considered as an interpolation node of the velocity field in the phase space. The velocities at these nodes are selected such that all the streamlines diverge to an attracting set imbedded in the subspace occupied by the cluster of examples. The synaptic interconnections are found from the minimization of the strength energy, while the node velocities play the role of constraints.

Zak, Michail↗
NASA NTRS1989

Unsupervised learning in neurodynamics using example-interaction approach

A new concept for unsupervised learning based upon examples introduced to the neural network is proposed. Each example is considered as an interpolation node of the velocity field in the phase space. The velocities at these nodes are selected such that all the streamlines diverge to an attracting set imbedded in the subspace occupied by the cluster of examples. The synaptic interconnections are found from the minimization of the strength energy, while the node velocities play the role of constraints.

Zak, Michail↗
NASA NTRS1987

An unsupervised feature extraction method for high dimensional image data compaction

A new on-line unsupervised feature extraction method for high-dimensional remotely sensed image data compaction is presented. This method can be utilized to solve the problem of data redundancy in scene representation by satellite-borne high resolution multispectral sensors. The algorithm first partitions the observation space into an exhaustive set of disjoint objects. Then, pixels that belong to an object are characterized by an object feature. Finally, the set of object features is used for data transmission and classification. The example results show that the performance with the compacted features provides a slight improvement in classification accuracy instead of any degradation. Also, the information extraction method does not need to be preceded by a data decompaction.

Ghassemian, Hassan↗
NASA NTRS1991

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

An unsupervised selection of polarimetric features useful for the segmentation and analysis of polarimetric synthetic aperture radar (SAR) data is presented. The technique is based on multidimensional clustering of the parameters composing the polarimetric covariance matrix of the data. Clustering is performed on the logarithm of these quantities. Once the polarimetric cluster centers have been determined, segmentation of the polarimetric data into regions is performed using a maximum likelihood polarimetric classifier. Segmentation maps are further improved using a Markov random field to describe the statistics of the regions and computing the maximum of the product of the local conditional densities. Examples with real polarimetric SAR imagery are given to illustrate the potential of this method.

Rignot, Eric↗
NASA NTRS1992

Unsupervised segmentation of polarimetric SAR data using the covariance matrix

A method for unsupervised segmentation of polarimetric synthetic aperture radar (SAR) data into classes of homogeneous microwave polarimetric backscatter characteristics is presented. Classes of polarimetric backscatter are selected on the basis of a multidimensional fuzzy clustering of the logarithm of the parameters composing the polarimetric covariance matrix. The clustering procedure uses both polarimetric amplitude and phase information, is adapted to the presence of image speckle, and does not require an arbitrary weighting of the different polarimetric channels; it also provides a partitioning of each data sample used for clustering into multiple clusters. Given the classes of polarimetric backscatter, the entire image is classified using a maximum a posteriori polarimetric classifier. Four-look polarimetric SAR complex data of lava flows and of sea ice acquired by the NASA/JPL airborne polarimetric radar (AIRSAR) are segmented using this technique. The results are discussed and compared with those obtained using supervised techniques.

Rignot, Eric J. M.↗
NASA NTRS1992

Analyst variability in labeling of unsupervised classifications

Analyst variability in the labeling of unsupervised classifications is tested for Landsat 5 Thematic Mapper image products covering two test sites in southern California. The accuracy of results are tested using samples from a photo interpreted base map of the area. The significance of differences between analysts is indicated by comparing Kappa statistics derived from error matrices. Analyst variability is found to be statistically significant in most cases. Certain analysts provided consistently better results for a given study area or degree of training. This work demonstrates the potential influence of analyst bias on what would otherwise seem to be a fairly objective method and suggests that controls for this subjectivity should be factored into experimental designs.

Mcgwire, Kenneth C.↗
NASA NTRS2001

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

LeMoigne, Jacqueline↗
NASA NTRS1997

Unsupervised, Robust Estimation-based Clustering for Multispectral Images

To prepare for the challenge of handling the archiving and querying of terabyte-sized scientific spatial databases, the NASA Goddard Space Flight Center's Applied Information Sciences Branch (AISB, Code 935) developed a number of characterization algorithms that rely on supervised clustering techniques. The research reported upon here has been aimed at continuing the evolution of some of these supervised techniques, namely the neural network and decision tree-based classifiers, plus extending the approach to incorporating unsupervised clustering algorithms, such as those based on robust estimation (RE) techniques. The algorithms developed under this task should be suited for use by the Intelligent Information Fusion System (IIFS) metadata extraction modules, and as such these algorithms must be fast, robust, and anytime in nature. Finally, so that the planner/schedule module of the IlFS can oversee the use and execution of these algorithms, all information required by the planner/scheduler must be provided to the IIFS development team to ensure the timely integration of these algorithms into the overall system.

Netanyahu, Nathan S.↗
NASA NTRS1999

Unsupervised classification of Space Acceleration Measurement System (SAMS) data using ART2-A

The Space Acceleration Measurement System (SAMS) has been developed by NASA to monitor the microgravity acceleration environment aboard the space shuttle. The amount of data collected by a SAMS unit during a shuttle mission is in the several gigabytes range. Adaptive Resonance Theory 2-A (ART2-A), an unsupervised neural network, has been used to cluster these data and to develop cause and effect relationships among disturbances and the acceleration environment. Using input patterns formed on the basis of power spectral densities (psd), data collected from two missions, STS-050 and STS-057, have been clustered.

STS-57 Shuttle Project↗
NASA NTRS1995

An Example of Unsupervised Networks Kohonen's Self-Organizing Feature Map

Kohonen's self-organizing feature map belongs to a class of unsupervised artificial neural network commonly referred to as topographic maps. It serves two purposes, the quantization and dimensionality reduction of date. A short description of its history and its biological context is given. We show that the inherent classification properties of the feature map make it a suitable candidate for solving the classification task in power system areas like load forecasting, fault diagnosis and security assessment.

Kohonen Feature Map↗
NASA NTRS2013

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of manmade radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short "transient" RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation.

Doran, G.↗
NASA NTRS2019

Utilization of Unsupervised Anomalies Detector as a Tool for Managing the TDRS Constellation at GSFC

NASA’s Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The mission of the TDRS constellation is to provide relay communications from low-earth orbiting spacecraft to the primary ground station at the White Sands Complex in Las Cruces, New Mexico. Major customers include the International Space Station and Hubble Space Telescope. The NASA Space Network project office at GSFC manages the constellation of spacecraft. The constellation is over 30 years old, and a wide range of technologies and manufacturing techniques are represented on-orbit. Since 1983, the TDRS constellation has recorded thousands of gigabytes of telemetry data. Spacecraft telemetry data has changed throughout the three generations of TDRS spacecraft, however each spacecraft has the same basic functions with some generational enhancements. The constellation includes several spacecraft that have significantly outlived the manufacturer's projected lifetime. This has provided NASA with a significant benefit in terms of return on investment, however it places a burden on efficient management of the assets for maximum life without permitting a TDRS spacecraft to become stranded in its geosynchronous orbital slot. Consequently, the highest level of attention is paid to systems whose failure could strand a TDRS spacecraft in orbit. In this paper, we proposed two stages of analyzing spacecraft anomalies using data mining (DM) to enhance on-going predictions of spacecraft life, subsystem performance, and analysis of subsystem anomalies. The first stage conducts the unsupervised anomaly detector to detect potential anomalies in real-time telemetry data. The second stage introduced telemetry weight (TW) to each telemetry parameter to determine which parameter caused the strongest anomaly. We will present case studies of some of these analyses and how the data can impact decisions on the management of the constellation.

Ma, Kenneth Y.↗
NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗
NASA NTRS2013

Characterizing Interference in Radio Astronomy Observations through Active and Unsupervised Learning

In the process of observing signals from astronomical sources, radio astronomers must mitigate the effects of man-made radio sources such as cell phones, satellites, aircraft, and observatory equipment. Radio frequency interference (RFI) often occurs as short bursts (< 1 ms) across a broad range of frequencies, and can be confused with signals from sources of interest such as pulsars. With ever-increasing volumes of data being produced by observatories, automated strategies are required to detect, classify, and characterize these short “transient” RFI events. We investigate an active learning approach in which an astronomer labels events that are most confusing to a classifier, minimizing the human effort required for classification. We also explore the use of unsupervised clustering techniques, which automatically group events into classes without user input. We apply these techniques to data from the Parkes Multibeam Pulsar Survey to characterize several million detected RFI events from over a thousand hours of observation

Doran, G.↗
NASA NTRS2024

Sensor Fault Detection in Smart Extraterrestrial Habitats Using Unsupervised Learning

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature–pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed.

Zixin Wang↗