The compressed sensing (CS) model of signal processing, while offering many unique advantages in terms of low-cost sensor design, poses interesting challenges for both signal acquisition and recovery, especially for signals of large size. In this work, we investigate how CS might be applied practically and efficiently in the context of natural video. We make use of a CS video acquisition approach in line with the popular single-pixel camera framework of blind, nonaptive, random sampling while proposing new approaches for the subsequent recovery of the video signal which leverage interrame redundancy to minimize recovery error. We introduce a method of approximation, which we term multihypothesis (MH) frame prediction, to create accurate frame predictions by comparing hypotheses drawn from the spatial domain of chosen reference frames to the non-overlapping, block-by-block CS measurements of subsequent frames. We accomplish this frame prediction via a novel distance-weighted Tikhonov regularization technique. We verify through our experiments that MH frame prediction via distance-weighted regularization provides state-of-the-art performance for the recovery of natural video sequences from blind CS measurements. The distance-weighted regularization we propose need not be limited to just frame prediction for CS video recovery, but may also be used in a variety of contexts where approximations must be generated from a set of hypotheses or training data. To show this, we apply our technique to supervised hyperspectral image (HSI) classification via a novel classifier we term the nearest regularized subspace (NRS) classifier. We show that the distance-weighted regularization used in the NRS method provides greater classification accuracy than state-of-the-art classifiers for supervised HSI classification tasks. We also propose two modifications to the core NRS classifier to improve its robustness to variation of input parameters and and to further increase its classification accuracy.

The goal of this study was to demonstrate the application of aerial imagery as a tool in detecting water quality indicators in a three mile segment of Tibbee Creek in, Clay County, Mississippi. Water samples from 10 transects were collected per sampling date over two periods in 2010 and 2011. Temperature and dissolved oxygen (DO) were measured at each point, and water samples were tested for turbidity and total suspended solids (TSS). Relative reflectance was extracted from high resolution (0.5 meter) multispectral aerial images. A regression model was developed for turbidity and TSS as a function of values for specific sampling dates. The best model was used to predict turbidity and TSS using datasets outside the original model date. The development of an appropriate predictive model for water quality assessment based on the relative reflectance of aerial imagery is affected by the quality of imagery and time of sampling.

No description available.

<p>Rivers are constantly undergoing change due to erosion and sedimentation along their banks. Although these processes generally occur gradually, flood events can significantly accelerate river migration, creating a risk for human life and infrastructure. As a result, it is important to identify river reaches that are prone to channel migration and determine the extent of migration. However, detailed information about river migration across entire river networks is not readily available. This study seeks to develop a Python-based tool that can generate river migration rasters across large watersheds using Landsat imagery. The methodology involves extracting the centerlines of river features in Landsat imagery using the Modified Normalized Difference Water Index (MNDWI) and the Skeletonize function available in the scikit-image library, followed by the application of the Particle Image Velocimetry (PIV) algorithm to compute the river channel migration. The PIV algorithm generates a set of migration rasters that are analyzed to extract the long-term migration of each of the reaches. The tool also creates intermediate outputs, such as the MNDWI raster, binary land-water raster, and skeletonized river centerlines, which can be further analyzed to gain insights into the river's behavior. The methodology is implemented in the Wabash and Lower Mississippi River Basins, and the tool's effectiveness is validated against manual measurements of the river migration available for the Wabash Basin. In addition, this study analyzes the correlation between long-term migration and various factors, such as reach sinuosity, drainage area, geology, and streamflow. The results of the analysis show that drainage area is highly correlated with river migration. The correlation results are compared with the prior literature, thereby serving to validate the developed framework. This framework has the potential to aid decision-makers and policymakers in identifying the long-term patterns of river channel migration, facilitating their efforts to plan for infrastructure resilience. By utilizing this methodology, river managers and other stakeholders can gain insights into river migration across large watersheds and identify areas that require further monitoring and management.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Despite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%.

This paper will discuss to what extent Emily Dickinson's heritage, environment, and experience formed her attitudes on religion and love, and will explain how successful she was in translating her intense emotional experience of love into poetry by examining her use of religious imagery.

Himalayan glaciers are key components of earth's cryosphere, acting as hydrological reservoirs vital to many human and natural systems. Most Himalayan glaciers are shrinking in response to changing climate, which will potentially impact water resources, natural hazards, sea level rise, and many other aspects. However, there is much uncertainty regarding the state of these glaciers, as direct field data are difficult to obtain. Accordingly, long-timespan remote sensing techniques are needed to measure changing glaciers, which have memory and often respond to climate on decadal timescales. This study uses declassified historical imagery from the Hexagon spy satellite database to fulfill this requirement. A new highly-automated, computer-vision based solution is used to extract historical terrain models from Hexagon imagery, which are used as a baseline to compute geomorphic change for glaciers in the Kingdom of Bhutan and Tibet Autonomous Region of the eastern Himalayas. In addition to glaciers, the new method is used to quantify changes resulting from the Thistle Creek Landslide (surface elevation changes resulting from the landslide show an average elevation decrease of 14.4 ± 4.3 meters in the source area, an increase of 17.6 ± 4.7 meters in the deposition area, and a decrease of 30.2 ± 5.1 meters resulting from a new roadcut) and Mount St. Helens eruption in western North America (results show an estimated 2.48 ± 0.03 km3 of material was excavated during the eruption-triggered debris slide). These additional results illustrate the applicability of Hexagon imagery to a variety of landscape processes. Regarding the primary application in the Himalayas, all studied glaciers show significant ice loss. Futhermore, the multi-decadal timespan reveals important aspects of glacier dynamics not detectable with temporally shorter datasets. Some glaciers exhibit inverted mass-balance gradients due to variations in debris-cover, while enhanced ice losses are prominent on glacier toes terminating in moraine-dammed proglacial lakes, resulting from calving caused by thermal undercutting. Remarkably, debris-covered glaciers show significant thinning despite insulating effects of the debris, likely due to poorly-understood ice cliff and melt pond mechanisms. The mean annual geodetic mass balance of 22 studied glaciers over a 32-year period is estimated to be -0.16 ± 0.03 m yr-1 water equivalent. Thus, these glaciers are not in equilibrium with current climate, and appear to be losing significant amounts of ice regardless of debris-cover.

No description available.

Aircraft-mounted cameras have potential to greatly increase the effectiveness of wilderness search and rescue efforts by collecting photographs or video of the search area. The more data that is collected, the more difficult it becomes to process it by visual inspection alone. This work presents a method for automatically detecting unusual objects in aerial video to assist people in locating signs of missing persons in wilderness areas. The detector presented here makes use of anomaly detection methods originally designed for hyperspectral imagery. Multiple anomaly detection methods are considered, implemented, and evaluated. These anomalies are then aggregated into spatiotemporal objects by using the video's inherent spatial and temporal redundancy. The results are therefore summarized into a list of unusual objects to enhance the search technician's video review interface. In the user study reported here, unusual objects found by the detector were overlaid on the video during review. This increased participants' ability to find relevant objects in a simulated search without significantly affecting the rate of false detection. Other effects and possible ways to improve the user interface are also discussed.

Rangeland monitoring is used by land managers for assessing multiple-use management practices on western rangelands. Managers benefit from improved monitoring methods that provide rapid, accurate, cost-effective, and robust measures of rangeland health and ecological trend. In this study, we used a supervised classification image analysis approach to estimate plant cover and bare ground by functional group that can be used to monitor and assess rangeland structure. High-resolution color infrared imagery taken of 40 research plots was acquired with a UltraCam X (UCX) digital camera during summer 2011. Ground estimates of cover were simultaneously collected by the Utah Division of Wildlife Resources' Range Trend Project field crew within these same areas. Image analysis was conducted using supervised classification to determine percent cover from Red, Green, Blue and infrared images. Classification accuracy and mean difference between cover estimates from remote sensed imagery and those obtained from the ground were compared using an accuracy assessment with Kappa statistic and a t-test analysis, respectively. Percent cover estimates from remote sensing ranged from underestimating the surface class (rock, pavement, and bare ground) by 27% to overestimating shrubs by less than 1% when compared to field-based measurements. Overall accuracy of the supervised classification was 91% with a kappa statistic of 0.88. The highest accuracy was observed when classifying surface values (bare ground, rock) which had a user's and producer's accuracy of 92% and 93%, respectively. Although surface cover varied significantly from field-based estimates, plant cover varied only slightly, giving managers an option to assess plant cover effectively and efficiently on greater temporal and spatial extents.