5 research outputs found
A Timelapse Camera Dataset and Markov Model of Dust Devil Activity at Eldorado Playa, Nevada, USA
We report a May-June 2015 survey of dust devil activity on a Nevada desert playa using an inexpensive digital timelapse camera. We discuss techniques for exploiting the large volume of data (∼32,700 images, made publicly-available) generated in these observations, similar to imaging from Mars landers and rovers, noting the diurnal image filesize variations as a useful quick-look metric of weather conditions. We present results from a semi-automated image classification: this classification is available to other workers, for example for benchmarking automated procedures. The acquisition of images at 1/min for some 36 days permits study of the diurnal variation of dust devil activity (e.g. 85% of the dust devil images [i.e. those images manually classified as showing dust devils] occur between 12:00 and 17:00; during the period of peak activity 13:00–15:00 about 7% of images contain well-defined dust devils of several meters diameter or larger). The data also permit the dependence of dust devil characteristics on ambient conditions. We construct a simple two-state Markov model for the occurrence and persistence of dust devils (a few per cent chance that new dust devil activity appears in the next image; and a ∼45% chance that activity stops) which may help inform strategies for acquiring and interpreting field observations
Semi-Supervised Learning for Mars Imagery Classification and Segmentation
With the progress of Mars exploration, numerous Mars image data are collected
and need to be analyzed. However, due to the imbalance and distortion of
Martian data, the performance of existing computer vision models is
unsatisfactory. In this paper, we introduce a semi-supervised framework for
machine vision on Mars and try to resolve two specific tasks: classification
and segmentation. Contrastive learning is a powerful representation learning
technique. However, there is too much information overlap between Martian data
samples, leading to a contradiction between contrastive learning and Martian
data. Our key idea is to reconcile this contradiction with the help of
annotations and further take advantage of unlabeled data to improve
performance. For classification, we propose to ignore inner-class pairs on
labeled data as well as neglect negative pairs on unlabeled data, forming
supervised inter-class contrastive learning and unsupervised similarity
learning. For segmentation, we extend supervised inter-class contrastive
learning into an element-wise mode and use online pseudo labels for supervision
on unlabeled areas. Experimental results show that our learning strategies can
improve the classification and segmentation models by a large margin and
outperform state-of-the-art approaches.Comment: Accepted by ACM Trans. on Multimedia Computing Communications and
Applications (TOMM
Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies
abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes.
This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201
Utilizing Science and Technology to Enhance a Future Planetary Mission: Applications to Europa
abstract: A thorough understanding of Europa's geology through the synergy of science and technology, by combining geologic mapping with autonomous onboard processing methods, enhances the science potential of future outer solar system missions. Mapping outlines the current state of knowledge of Europa's surface and near sub-surface, indicates the prevalence of distinctive geologic features, and enables a uniform perspective of formation mechanisms responsible for generating those features. I have produced a global geologic map of Europa at 1:15 million scale and appraised formation scenarios with respect to conditions necessary to produce observed morphologies and variability of those conditions over Europa's visible geologic history. Mapping identifies areas of interest relevant for autonomous study; it serves as an index for change detection and classification and aids pre-encounter targeting. Therefore, determining the detectability of geophysical activity is essential. Activity is evident by the presence of volcanic plumes or outgassing, disrupted surface morphologies, or changes in morphology, color, temperature, or composition; these characteristics reflect important constraints on the interior dynamics and evolutions of planetary bodies. By adapting machine learning and data mining techniques to signatures of plumes, morphology, and spectra, I have successfully demonstrated autonomous rule-based response and detection, identification, and classification of known events and features on outer planetary bodies using the following methods: 1. Edge-detection, which identifies the planetary horizon and highlights features extending beyond the limb; 2. Spectral matching using a superpixel endmember detection algorithm that identifies mean spectral signatures; and 3. Scale invariant feature transforms combined with supervised classification, which examines brightness gradients throughout an image, highlights extreme gradient regions, and classifies those regions based on a manually selected library of features. I have demonstrated autonomous: detection of volcanic plumes or jets at Io, Enceladus, and several comets, correlation between spectral signatures and morphological appearances of Europa's individual tectonic features, detection of ≤94% of known transient events on multiple planetary bodies, and classification of similar geologic features. Applying these results to conditions expected for Europa enables a prediction of the potential for detection and recommendations for mission concepts to increase the science return and efficiency of future missions to observe Europa.Dissertation/ThesisPh.D. Geological Sciences 201