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Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System
The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.
Evaluation of the NASA Artemis Regions of Interest for ISRU Water Mine Potential
The NASA Artemis Campaign has a stated goal to return to the Moon to maintain a sustainable presence; In-Situ Resource Utilization (ISRU) is a key part of sustainability. The regions of interest identified for the Artemis campaign are at Lunar the South Pole where water ice, a valuable resource for ISRU, has been identified. As such, a preliminary evaluation of the ISRU ice mining potential has been performed for of these regions of interest. A set of ground rules for this evaluation were developed to align with current assumptions for customer needs, hardware capabilities, an initially limited infrastructure, and lunar environments/terrain. These ground rules, and the evaluation of six regions of interest, are presented here. The site selections (ISRU and customer assets) and their associated traverses are notional and were intended only to provide a broad preliminary evaluation of the water ISRU potential of the regions. Evaluation of these regions are subject to change as decisions regarding utilization are made. Water ISRU is possible at all regions, though the degree to which each criterion are met is variable. The two regions near Shackleton ranked highest in this evaluation, while the de Gerlache region presented the most difficulties meeting the current criteria. The regions were not explicitly ranked due to the nuances associated with the high number of variables but evaluation summaries of each are presented. It should also be noted that all ISRU ‘mine’ sites in this analysis focused on smaller (few kilometer) size permanently shadowed regions (PSRs). This was necessary to meet proximity requirements between these PSRs and the highly illuminated regions needed for customers and ISRU processing. The areas identified in this study are meant to focus exploration and reconnaissance efforts needed to better evaluate the ISRU potential.
Evaluation of the NASA Artemis Regions of Interest for ISRU Water Mine Potential
The NASA Artemis Campaign has a stated goal to return to the Moon to maintain a sustainable presence; In-Situ Resource Utilization (ISRU) is a key part of sustainability. The regions of interest identified for the Artemis campaign are at Lunar the South Pole where water ice, a valuable resource for ISRU, has been identified. As such, a preliminary evaluation of the ISRU ice mining potential has been performed for of these regions of interest. A set of ground rules for this evaluation were developed to align with current assumptions for customer needs, hardware capabilities, an initially limited infrastructure, and lunar environments/terrain. These ground rules, and the evaluation of six regions of interest, are presented here. The site selections (ISRU and customer assets) and their associated traverses are notional and were intended only to provide a broad preliminary evaluation of the water ISRU potential of the regions. Evaluation of these regions are subject to change as decisions regarding utilization are made. Water ISRU is possible at all regions, though the degree to which each criterion are met is variable. The two regions near Shackleton ranked highest in this evaluation, while the de Gerlache region presented the most difficulties meeting the current criteria. The regions were not explicitly ranked due to the nuances associated with the high number of variables but evaluation summaries of each are presented. It should also be noted that all ISRU ‘mine’ sites in this analysis focused on smaller (few kilometer) size permanently shadowed regions (PSRs). This was necessary to meet proximity requirements between these PSRs and the highly illuminated regions needed for customers and ISRU processing. The areas identified in this study are meant to focus exploration and reconnaissance efforts needed to better evaluate the ISRU potential.
Optimal band selection for dimensionality reduction of hyperspectral imagery
Hyperspectral images have many bands requiring significant computational power for machine interpretation. During image pre-processing, regions of interest that warrant full examination need to be identified quickly. One technique for speeding up the processing is to use only a small subset of bands to determine the 'interesting' regions. The problem addressed here is how to determine the fewest bands required to achieve a specified performance goal for pixel classification. The band selection problem has been addressed previously Chen et al., Ghassemian et al., Henderson et al., and Kim et al.. Some popular techniques for reducing the dimensionality of a feature space, such as principal components analysis, reduce dimensionality by computing new features that are linear combinations of the original features. However, such approaches require measuring and processing all the available bands before the dimensionality is reduced. Our approach, adapted from previous multidimensional signal analysis research, is simpler and achieves dimensionality reduction by selecting bands. Feature selection algorithms are used to determine which combination of bands has the lowest probability of pixel misclassification. Two elements required by this approach are a choice of objective function and a choice of search strategy.
Autonomous Image Processing Algorithms Locate Region-of-Interests: The Mars Rover Application
In this report, we demonstrate that bottom-up IPA's, image-processing algorithms, can perform a new visual task to select and locate Regions-Of-Interests (ROIs). This task has been defined on the basis of a theory of top-down human vision, the scanpath theory. Further, using measures, Sp and Ss, the similarity of location and ordering, respectively, developed over the years in studying human perception and the active looking role of eye movements, we could quantify the efficient and efficacious manner that IPAs can imitate human vision in located ROIS. The means to quantitatively evaluate IPA performance has been an important part of our study. In fact, these measures were essential in choosing from the initial wide variety of IPAS, that particular one that best serves for a type of picture and for a required task. It should be emphasized that the selection of efficient IPAs has depended upon their correlation with actual human chosen ROIs for the same type of picture and for the same required task accomplishment.
Science-based Region-of-Interest Image Compression
As the number of currently active space missions increases, so does competition for Deep Space Network (DSN) resources. Even given unbounded DSN time, power and weight constraints onboard the spacecraft limit the maximum possible data transmission rate. These factors highlight a critical need for very effective data compression schemes. Images tend to be the most bandwidth-intensive data, so image compression methods are particularly valuable. In this paper, we describe a method for prioritizing regions in an image based on their scientific value. Using a wavelet compression method that can incorporate priority information, we ensure that the highest priority regions are transmitted with the highest fidelity.
Is a Linear or a Walkabout Protocol More Efficient When Using a Rover to Choose Biologically Relevant Samples in a Small Region of Interest?
We conducted a field test at a potential Mars analog site to provide insight into planning for future robotic missions such as Mars 2020, where science operations must facilitate efficient choice of biologically relevant sampling locations. We compared two data acquisition and decision-making protocols currently used by Mars Science Laboratory: (1) a linear approach, where sites are examined as they are encountered and (2) a walkabout approach, in which the field site is first examined with remote rover instruments to gain an understanding of regional context followed by deployment of time- and power-intensive contact and sampling instruments on a smaller subset of locations. The walkabout method was advantageous in terms of both the time required to execute and a greater confidence in results and interpretations, leading to enhanced ability to tailor follow-on observations to better address key science and sampling goals. This advantage is directly linked to the walkabout method's ability to provide broad geological context earlier in the science analysis process. For Mars 2020, and specifically for small regions to be explored (e.g., <1 sq. km), we recommend that the walkabout approach be considered where possible, to provide early context and time for the science team to develop a coherent suite of hypotheses and robust ways to test them.
Windowed region-of-interest non-uniformity correction and range walk error correction of a 3D flash LiDAR camera
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Automated Image Registration Using Morphological Region of Interest Feature Extraction
With the recent explosion in the amount of remotely sensed imagery and the corresponding interest in temporal change detection and modeling, image registration has become increasingly important as a necessary first step in the integration of multi-temporal and multi-sensor data for applications such as the analysis of seasonal and annual global climate changes, as well as land use/cover changes. The task of image registration can be divided into two major components: (1) the extraction of control points or features from images; and (2) the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual control feature extraction can be subjective and extremely time consuming, and often results in few usable points. Automated feature extraction is a solution to this problem, where desired target features are invariant, and represent evenly distributed landmarks such as edges, corners and line intersections. In this paper, we develop a novel automated registration approach based on the following steps. First, a mathematical morphology (MM)-based method is used to obtain a scale-orientation morphological profile at each image pixel. Next, a spectral dissimilarity metric such as the spectral information divergence is applied for automated extraction of landmark chips, followed by an initial approximate matching. This initial condition is then refined using a hierarchical robust feature matching (RFM) procedure. Experimental results reveal that the proposed registration technique offers a robust solution in the presence of seasonal changes and other interfering factors. Keywords-Automated image registration, multi-temporal imagery, mathematical morphology, robust feature matching.
A high frame rate CCD camera with region-of-interest capability
This paper presents the design and preliminary results of a custom high-speed CCD camera utilizing a Texas Instruments TC237 CCD imager chip with sub-frame window read out.
Multi-Stage System for Automatic Target Recognition
A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.
Defining the Middle Corona
The middle corona, the region roughly spanning heliocentric distances from 1.5 to 6 solar radii, encompasses almost all of the influential physical transitions and processes that govern the behavior of coronal outflow into the heliosphere. The solar wind, eruptions, and flows pass through the region, and they are shaped by it. Importantly, the region also modulates inflow from above that can drive dynamic changes at lower heights in the inner corona. Consequently, the middle corona is essential for comprehensively connecting the corona to the heliosphere and for developing corresponding global models. Nonetheless, because it is challenging to observe, the region has been poorly studied by both major solar remote-sensing and in-situ missions and instruments, extending back to the Solar and Heliospheric Observatory (SOHO) era. Thanks to recent advances in instrumentation, observational processing techniques, and a realization of the importance of the region, interest in the middle corona has increased. Although the region cannot be intrinsically separated from other regions of the solar atmosphere, there has emerged a need to define the region in terms of its location and extension in the solar atmosphere, its composition, the physical transitions that it covers, and the underlying physics believed to shape the region. This article aims to define the middle corona, its physical characteristics, and give an overview of the processes that occur there.
Atmospheric effects on the mapping of Martian thermal inertia and thermally derived albedo
We examine the effects of a dusty CO2 atmosphere on the thermal inertia and thermally derived albedo of Mars and we present a new map of thermal inertias. This new map was produced using a coupled surface atmosphere (CSA) model, dust opacities from Viking infrared thermal mapper (IRTM) data, and CO2 columns based on topography. The CSA model thermal inertias are smaller than the 2% model thermal inertias, with the difference largest at large thermal inertia. Although the difference between the thermal inertias obtained with the two models is moderate for much of the region studied, it is largest in regions of either high dust opacity or of topographic lows, including the Viking Lander 1 site and some geologically interesting regions. The CSA model thermally derived albedos do not acurately predict the IRTM measured albedos and are very similar to the thermally derived albedos obtained with models making the 2% assumption.
Atmospheric effects on the mapping of Martian thermal inertia and thermally derived albedo
We examine the effects of a dusty C02 atmosphere on the thermal inertia and thermally derived albedo of Mars and we present a new map of thermal inertias. This new map was produced using a coupled surface atmosphere (CSA) model, dust opacities from Viking infrared thermal mapper (IRTM) data, and C02 columns based on topography. The CSA model thermal inertias are smaller than the 2% model thermal inertias, with the difference largest at large thermal inertia. Although the difference between the thermal inertias obtained with the two models is moderate for much of the region studied, it is largest in regions of either high dust opacity or of topographic lows, including the Viking Lander 1 site and some geologically interesting regions. The CSA model thermally derived albedos do not accurately predict the IRTM measured albedos and are very similar to the thermally derived albedos obtained with models making the 2% assumption.
Solar Forced Dansgaard/Oeschger Events?
Climate records for the last ice age (which ended 11,500 years ago) show enormous climate fluctuations in the North Atlantic region - the so-called Dansgaard/Oeschger events. During these events air temperatures in Greenland changed on the order of 10 degrees Celsius within a few decades. These changes were attributed to shifts in ocean circulation which influences the warm water supply from lower latitudes to the North Atlantic region. Interestingly, the rapid warmings tend to recur approximately every 1500 years or multiples thereof. This has led researchers to speculate about an external cause for these changes with the variable Sun being one possible candidate. Support for this hypothesis came from climate reconstructions, which suggested that the Sun influenced the climate in the North Atlantic region on these time scales during the last approximately 12,000 years of relatively stable Holocene climate. However, Be-10 measurements in ice cores do not indicate that the Sun caused or triggered the Dansgaard/Oeschger events. Depending on the solar magnetic shielding more or less Be-10 is produced in the Earth's atmosphere. Therefore, 10Be can be used as a proxy for solar activity changes. Since Be-10 can be measured in ice cores, it is possible to compare the variable solar forcing directly with the climate record from the same ice core. This removes any uncertainties in the relative dating, and the solar-climate link can be reliably studied. Notwithstanding that some Dansgaard/Oeschger warmings could be related to increased solar activity, there is no indication that this is the case for all of the Dansgaard/Oeschger events. Therefore, during the last ice age the Be-10 and ice core climate data do not indicate a persistent solar influence on North Atlantic climate.
A Data Exploration Tool for Large Sets of Spectra
We present an exploration tool for very large spectrum data sets such as the SDSS (Sloan Digital Sky Survey), LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope), and 4MOST (4-meter Multi-Object Spectroscopic Telescope) data sets. The tool works in two stages: the first uses batch processing and the second runs interactively. The latter employs the NASA hyperwall, a configuration of 128 workstation displays (8 by 16 array) controlled by a parallelized software suite running on NASA's Pleiades supercomputer. The stellar subset of the Sloan Digital Sky Survey, DR10, was chosen to show how the our tool may be used. In stage one, SDSS files for 569,740 stars are processed through our data pipeline. The pipeline fits each spectrum using an iterative continuum algorithm, distinguishing emission from absorption and handling molecular absorption bands correctly. It then measures 1659 discrete atomic and molecular spectral features that were carefully preselected based on their likelihood of being visible at some spectral type. The depths relative to the local continuum at each feature wavelength are determined for each spectrum: these depths, the local S/N (signal to noise ratio) level, and DR10-supplied variables such as magnitudes, colors, positions, and radial velocities are the basic measured quantities used on the hyperwall. In stage two, each hyperwall panel is used to display a 2-D scatter plot showing the depth of feature A vs the depth of feature B for all of the stars. A and B change from panel to panel. The relationships between the various (A,B) strengths and any distinctive clustering are immediately apparent when examining and inter-comparing the different panels on the hyperwall. The interactive software allows the user to select the stars in any interesting region of any 2-D plot on the hyperwall, immediately rendering the same stars on all the other 2-D plots in a unique color. The process may be repeated multiple times, each selection displaying a distinctive color on all the plots. At any time, the spectra of the selected stars may be examined in detail on a connected workstation display. We illustrate how our approach allows us to quickly isolate and examine such interesting stellar subsets as EMP (Extremely Metal‐Poor) stars, CV (Cataclymic Variable) stars and C (Carbon)-rich stars.
Examination of Regional Trends in Low Level Cloud Properties Found in the Aqua-MODIS Satellite Record
Clouds have a pronounced influence on the Earth?s climate. Relative to cloud free conditions, they cool the planet by increasing the amount of solar radiation reflected back to space and reducing the amount of sunlight reaching the surface, but they warm the planet by decreasing the amount of thermal infrared radiation escaping to space and increasing the amount reaching the surface (a greenhouse effect). The global mean net cloud radiative effect (CRE) is about -20 W/m2, a cooling effect at both the top-ofatmosphere and surface. Given the magnitude of CRE?s, it is expected that changes in cloud properties could be a significant factor in climate change due to anthropogenic forcing?s, yet cloud feedbacks are not well known and remain one of the largest uncertainties in climate prediction. This paper explores relationships between coincident observations of atmospheric aerosols, clouds and radiation derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and from the Clouds and the Earth?s Radiant Energy System (CERES) instruments on the Aqua satellite. We investigate several interesting regional trends that have emerged in the nearly 18-year satellite record that suggest correlation between changes in low-level cloud properties and changes in aerosol optical depth that may be associated with changes in pollution emissions and possibly with other factors. MERRA reanalysis of meteorological conditions and aerosol particulate species are investigated to help better understand the potential mechanisms responsible for the observed cloud property trends. Finally, we analyze a new CERES flux by cloud type dataset in order to try and isolate the associated trends in low-level cloud radiative effects. It is anticipated that this study using long-term observations of clouds, aerosols and radiative fluxes combined with model reanalysis data will contribute to an improved understanding of cloud climate feedbacks.