KEYWORDS: Data modeling, Land cover, Forest fires, Climatology, Education and training, Vegetation, Neural networks, Deep learning, Combustion, Meteorology
Over the last several decades, large wildfires have become increasingly common across the United States causing a disproportionate impact on forest health and function, human well-being, and the economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011 to 2020) using a wide array of meteorological, land cover, and topographical features in a deep neural network model. A total of 4538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43% of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the weather, land cover, and elevation of the study area as indicated from their SHapley Additive exPlanations values. Overall, different variants of data-driven models and their results could provide useful guidance in managing landscapes for large wildfires under changing climate and disturbance regimes.
Volunteer cotton (VC) plants growing in fields of inter-seasonal crop like corn can act as host for the boll weevil pests; therefore, they need to be detected, located, and sprayed to prevent reinfestation of the pest in the following season. However, detecting the VC plants in corn fields has always been challenging as they remain hidden under the canopy and appear spectrally similar during the early growth phase. In this paper, we show that deep learning based YOLOv3 model can be used to detect VC plants in early growth corn field on RGB aerial images collected remotely by unmanned aircraft system (UAS) at a mean average precision (mAP) of 90.60% and F1-score of 86.35%. The approach of using deep learning to detect VC plants demonstrates its ability to be used for near real-time detection thereby expediting the management aspects of Texas Boll Weevil Eradication Program.
Error estimation is a key aspect of statistical pattern recognition. The true classification error rate is usually unavailable since it depends on the unknown feature-label distribution. Hence, one needs to estimate the error rate from the available sample data. This paper presents a concise, mathematically rigorous review of the subject of error estimation in statistical pattern recognition, pointing to the pitfalls that arise in small-sample settings due to the use of "rules of thumb" and a neglect for proper mathematical understanding of the problem.
We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity, and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. Detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.
KEYWORDS: Detection and tracking algorithms, Target detection, Land mines, Signal to noise ratio, Mathematical morphology, Reconstruction algorithms, Image filtering, Multispectral imaging, Visualization, Binary data
Automatic mine detection is a critical issue in battle field management. This is expected to lead to better technologies that provide accurate and reliable detection of mines embedded in clutter. In this paper, we review a procedure for automatic mine detection in multispectral data provided by the Coastal Battlefield Reconnaissance and Analysis (COBRA) program. Our procedure is essentially a two-step method that employs the Maximum Noise Fraction (MNF) transform, a powerful enhancement tool for multispectral data, combined with nonlinear morphological operators that do the actual detection. Mathematical morphology is also used to account for the critical step of clutter estimation required by the MNF transform. Results obtained with available, truthed data, show the high success of the proposed method in meeting performance requirements. A low number of midsections is observed, whereas only a small number of false alarms is introduced by the algorithm. The results are better than the ones obtained by means of a constant false alarm rate (CFAR) algorithm provided along with the data.
This paper presents mathematical morphology tools for 3D image analysis, namely, the geodesic granulometries and the neck histogram. The family of openings which constitutes the geodesic granulometries is parameterized by the radius of the digital disks utilized as structuring elements. We demonstrate the validity of the granulometry thus obtained. The resulting granulometric distributions are determined by the underlying metric associated with the digital disks. Next we propose an algorithm to compute the neck histogram, which is an analysis tool that gives statistical information concerning the occurrence of constrictions in the object studied. Finally, we demonstrate the applicaiton of the proposed analysis tools in the characterization of a 3D experimental sample designed as a model for a porous medium.
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