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Vehicle maneuverability is often supported in low-light scenarios through infrared (IR) imagery. However, if the imagery contains little temperature gradient, the raw images are less applicable. In order to maximize image effectiveness, a genetic algorithm (GA) is employed to explore various contrast enhancement operators to determine an optimal sequence of contrast enhancements. We propose a new image quality evaluator that incorporates the performance of a deep learning-based object detector and considers image spatial context through cell-structured configurations. The proposed technique is assessed both qualitatively and quantitatively for the task of maneuverability hazard detection.

Object detection remains an important and ever-present component of computer vision applications. While deep learning has been the focal point for much of the research actively being conducted in this area, there still exists certain applications in which such a sophisticated and complex system is not required. For example, if a very specific object or set of objects are desired to be automatically identified, and these objects' appearances are known a priori, then a much simpler and more straightforward approach known as matched filtering, or template matching, can be a very accurate and powerful tool to employ for object detection. In our previous work, we investigated using machine learning, specifically, the improved Evolution COnstructed features framework, to identify (near-) optimal templates for matched filtering given a specific problem. Herein, we explore how different search algorithms, e.g., genetic algorithm, particle swarm optimization, gravitational search algorithm, can derive not only (near-) optimal templates, but also promote templates that are more efficient. Specifically, given a defined template for a particular object of interest, can these search algorithms identify a subset of information that enables more efficient detection algorithms while minimizing degradation of detection performance. Performance is assessed in the context of algorithm efficiency, accuracy of the object detection algorithm and its associated false alarm rate, and search algorithm performance. Experiments are conducted on handpicked images of commercial aircraft from the xView dataset | one of the largest publicly available datasets of overhead imagery.

One aspect of the well-being of a military unit depends on its ability to reliably detect threats and properly prepare for them. While a given sensor mounted on a ground vehicle can adequately capture threats in some scenarios, its viewpoint can be quite limiting. A potential solution to these limitations is mounting the sensor onto an unmanned aerial vehicle (UAV) to provide a more holistic view of the scene. However, this new perspective creates challenges unique to it. Herein, we investigate the performance of an RGB sensor mounted onto a UAV for object detection and classification to enable advanced situational awareness for a manned/unmanned ground vehicle trailing the UAV. To do this, we perform transfer learning with state-of-the-art deep learning models, e.g., ResNet50, Inception-v3. While object detection with machine learning has been actively researched, even on remotely sensed imagery, most of it has been through the context of scene classification. Therefore, it is worthwhile to explore the implications of this new camera perspective on the performance of object detection. Performance is assessed via route-based cross-validation collected by the U.S. Army ERDC at a test site spanning multiple days.

In recent years, many financial institutions have implemented machine learning applications to better predict stock prices. Computational methods such as regression or classification learners give financial analysts the ability to predict a stock’s future price using historical data. More recently, sentiment analysis has been used to gather public opinion in hopes that it will provide meaningful information which would allow more accurate prediction models. In this work, we use both historical data and sentiment analysis scores to investigate whether sentiment analysis can improve performance of stock forecasting.

Feature extraction is widely considered one of the most critical components to classification performance in computer vision. In the past, human-designed features, such as the histogram of oriented gradients, were used for extracting statistically rich features. Recently, there has been a movement away from human-designed features to machine-learned features. Herein, we propose a novel genetic programming (GP) approach, coined GOOFeD, to automatically generate discriminative-rich features for image classification. This is achieved by greatly advancing GP in three ways: (1) promoting population diversity and redundancy removal, (2) introducing a unique adaptive mutation approach, and (3) controlling tree bloat through a new crossover technique. These improvements also lead to a population size required for learning that is smaller than that commonly used in the literature. To assess performance, GOOFeD is tested on the MIT urban and nature scene data set and a real-world buried explosive hazard data set. Experiments verify that, in terms of classification accuracy, GOOFeD outperforms many of the state-of-the-art human-designed features and feature learning techniques.

Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising., Comment: 6 pages, 4 figures, published in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Object recognition is a critical component in most computer vision applications, specifically image classification tasks. Often, it is desired to design an approach that either learns from the data directly or extracts discriminative features from the imagery that can be used for object classification. Most active research in the field of computer vision is concerned with machine learning at some level, whether it be a completely automated process from start to finish via deep learning strategies, or the extraction of human-derived features from the imagery that is subjected to a machine learning-based classifier. However, there are numerous applications in which a particular known object is of interest. In such a setting where a relatively specific object and scene are known a priori, one can develop an extremely robust automatic target recognition (ATR) system using matched filtering. Herein, we consider the use of machine learning to help identify a near-optimal template for matched filtering for a given problem. Specifically, the improved Evolution Constructed (iECO) framework is employed to learn the discriminative target signature(s) to define the template that leads to improved ATR performance in terms of accuracy and a reduced false alarm rate (FAR). Experiments are conducted on ideal synthetic midwave infrared imagery, and results are reported via receiver operating characteristic curves.

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations in order to understand relevant object phenomena. It has been shown in a previous work that deep learning may be used to increase the efficiency of creating such datasets by providing estimations comparable to simulation results. In this work, we further investigate the utility of deep learning for electromagnetic simulation prediction by adding to the existing training and testing dataset while also incorporating additional material properties. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to further investigate the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Additionally, performance is compared for different data pre-processing techniques focused on data reduction.

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations. Because these simulations are often computationally intensive, valuable resources are required to perform the simulations in an efficient and timely manner, which is not always freely available or accessible. In this work, we investigate the utility of deep learning for electromagnetic simulation prediction. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. Such a system would be trained in an offline setting and consequently enable rapid bistatic radar cross section predictions for new objects in the future. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to learn the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Several simple objects are investigated and a thorough statistical analysis will be used to assess the performance of our proposed method.

Synthetic aperture radar (SAR) benefits from persistent imaging capabilities that are not reliant on factors such as weather or time of day. One area that may benefit from readily available imaging capabilities is road damage detection and assessment occurring from disasters such as earthquakes, sinkholes, or mudslides. This work investigates the performance of a pre-screener for an automatic detection system used to identify locations and quantify the severity of road damage present in SAR imagery. The proposed pre-screener is comprised of two components: advanced image processing and classification. Image processing is used to condition the data, removing non-pertinent information from the imagery which helps the classifier achieve better performance. Specifically, we utilize shearlets; these are powerful filters that capture anisotropic features with good localization and high directional sensitivity. Classification is achieved through the use of a convolutional neural network, and performance is reported as classification accuracy. Experiments are conducted on satellite SAR imagery. Specifically, we investigate Sentinel-1 imagery containing both damaged and non-damaged roads.

One promising technique for improving tunnel detection is the use of spotlight synthetic aperture radar (SL-SAR) in conjunction with focusing techniques. Still, clutter arises from surface variations while severe attenuation of the target signal occurs due to the dielectric properties of the soil. To combat these ill-effects, this work aims to improve imaging and detection of underground tunnels by examining the feasibility of matched illumination waveform design for tunnel detection applications. The tunnel impulse response is incorporated in an optimum waveform derivation scheme which aims to maximize the signal-to-interference and noise ratio (SINR) at the receiver output. Numerical electromagnetic simulations are used to consider wave propagation in realistic soil scenarios which include uniform and non-uniform moisture profiles. It is demonstrated that by considering matched illumination waveforms for transmission in SL-SAR systems, an improvement in the detection and imaging capabilities is achieved through enhanced SINR.