As the demand for real-time signal processing increases in various fields, such as healthcare, artificial intelligence, machine learning, and scientific research, there is a need for more efficient methods to analyze large amounts of data. To address this challenge and explore the opportunities to accelerate different signal processing algorithms, this paper proposes the integration of graphics processing units (GPUs) with database management systems (DBMS) using the PostgreSQL server programming interface (SPI). The performance of the proposed method is evaluated by comparing central processing unit (CPU) and GPU approaches for feature extraction using a data processing pipeline for heart rate estimation from hydraulic bed sensor data. Furthermore, the paper analyzes timing metrics, usability, adaptability, and discusses precision differences between CPU and GPU code by performing different thread and block configurations.

With the increasing interconnection of smart sensors in long-term care facilities, the amount of data available for multi-modal Big Data analytics is advantageous. Using raw smart sensor data, researchers can derive and extract physiological features useful for health monitoring. Nonetheless, with the immense amount of smart sensor data, the ability to utilize these data for multi-modal analytics as the data grows presents a great challenge for researchers and long-term care facilities. This paper proposes a database design system for multi-modal derived time-series featured data (respiration and restlessness) by using techniques such as hierarchical time-indexed databases and dense numerical array storage. We present evaluations and findings for our proposed database system design for multi-modal time-series feature data to assess the various performance characteristics in data access time, storage, and usability; demonstrating an extremely scalable design and simple integration with existing analytic tools via SQL interfaces. Furthermore, we introduce a data-processing pipeline enabling Big Data analytics for multi-modal time-series feature data.

We are currently experiencing a deluge of high-resolution electroptical (HR-EO) remote sensing images which can be leveraged for a diverse set of applications, ranging from environmental monitoring to defense and security applications. One of the most challenging application domains is the use of machine learning techniques, such as computer vision, for the enhancement and automation of geospatial big data analytics. In this work, we present techniques for extending deep convolutional neural networks (DCNN) from 3-band color imagery to 4-band and 8-band multispectral remote sensing imagery. Performance comparisons are conducted between DCNN for 3-, 4-, and 8-band imagery data using the Functional Map of the World dataset. In particular, we investigate five distinct DCNN architectures for classification, and show that utilizing more input channels of the imagery typically has a positive impact on classification metrics such as F1-score and raw accuracy. Herein, we evaluated two methods for initializing the DCNN convolutional filters with 4-or 8-band inputs via transfer learning from networks trained on 3-band (RGB) images and show these methods are superior to random initialization. Our findings indicate that additional spectral bands have varied benefits, depending on the image class, where challenging classes saw minimal improvements. We detail these findings along with insights into the implications for multispectral DCNN in particular geospatial big data analytics use-cases.

In the last decade, several high resolution remote sensing benchmark datasets have been developed and publicly released. These datasets, while diverse in design, lack the required intra-class variation for high-performing, machine-assisted visual analytics. More specifically, the disparate datasets are suitable for small, closed system evaluation; however they are not well suited for training of computer vision models that are robust in real-world, non-closed environments encountered in true remote sensing applications. To that end, a benchmark meta-dataset (MDS) was developed to facilitate the training of models for machine-assisted visual analytics. Four existing benchmark datasets were combined to build the original MDS, which excelled for training models for both classification and broad area search applications. In this work, we evaluate an enhanced version of the MDS, MDSv2, by integrating co-occurring classes of two additional recently released, publicly available challenge datasets: xView and Functional Map of the World (FMoW). The MDSv2 has 33 classes with 87,470 total samples. We investigate the utility of three neural architecture search (NAS) deep learning architectures on the MDSv2 for both classification and machine-assisted visual analytics. The NAS models trained with the MDSv2 are able to achieve an average F1 of 98.01% and a powerful 0.934 scanning precision.

Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the detection performance of a larger complex feature. A wide variety of experiments were conducted using the public domain xView dataset to develop and evaluate multiple fusion strategies to improve the detection of Construction Sites using DNN detections of constitutive/component objects commonly associated with construction activity, e.g. cement mixers, dump trucks, etc. The results demonstrate that spatial fusion of multi-scale component object DNN detections can reduce the total detection error rate of Construction Sites by ~30-40%. The best results were obtained when local spatial clustering was used to reduce noise in component vehicle object detections generated by scanning candidate Construction Site locations. This multi-scale spatial fusion approach can be easily extended to improve detection performance in a wide variety of other challenging feature/object search and detection problems in large-scale remote sensing image datasets.

Object detection is a challenging but important task for computer vision, and this is especially true in the remote sensing domain where data collections may bring billions of pixels in a single image. There are many methods for object detection, but in recent years the You Only Look Once (YOLO) algorithm has become a leading technique, gaining popularity due to its ability to perform real-time object detection. While YOLO and its successors have shown excellent results in realtime detection, there are many object detection tasks that require better precision, and do not require real-time detection. In this paper, YOLOv3 is compared to other deep neural networks (DNN) for detecting Vehicle Groups in very high resolution remote sensing imagery (VHR-RSI). A unique centerpoint-based dataset is developed by leveraging a novel data framework and combining quality assured chips with regions of interest in the XView Challenge Dataset. This dataset is then used to train state of the art models including two Neural Architecture Search (NAS) variant DNN for object detection. Additionally, a blind test set is developed to further compare our methods with the YOLOv3 algorithm. The results shows that our method detects vehicle groups with a lower false positive rate (FPR) and better true positive rate (TPR) than state-of-the-art YOLOv3 models for the blind test set; achieving a reduction in error rate of 26.70% over YOLOv3 in F1 Score on the blind test set.

Object detection and localization within high-resolution remote sensing imagery (HR-RSI) is a challenging task for a variety of reasons, such as the complexity and clutter of the image scene and the compactness of the intermixed object classes. Even the most comprehensive training datasets cannot adequately account for the rich diversity and complexity of anthropogenic objects and their contextual settings in large-scale HR-RSI collections. Recent approaches using deep learning techniques include bounding box approaches (e.g., YOLO), object nomination then recognition (e.g., R-CNN), and post-detection object localization of deep neural network detections. Herein, we propose a novel technique that leverages heterogeneous superpixels and deep neural feature extraction to classify the superpixel segmentation through relational analysis. In this preliminary research, we demonstrate the validity of this approach for object detection and localization, as well as its suitability for identifying the irregular shapes of objects (as opposed to a bounding box). Experiments are performed using a sub-set of the xView benchmark dataset with a goal of spearheading future techniques in cluttered scene object recognition that allows deep feature extractors to have more focus on the target objects instead of the surrounding area or nearby object pixels.