Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Feature extraction in geobiophysical modeling of mining activity impacting Dewey Lake, Kentucky

Surface coal mining activity has effects on watershed and lake morphology within the Appalachian Mountains that continue to be problematic. A watershed’s natural dynamic balance has been subject to the influence of various natural and anthropogenic parameters such as mining sediment transport, wind, wave effects and currents. Many techniques have been developed to improve image processing in geobiophysical modeling, which can assist scientists, government officials, and industry personal with decisions affecting environmental concerns. One of the more advanced techniques involves 3D visualization and geobiophysical modeling. This process was used in combining remotely sensed digital aerial imagery with Digital Elevation Models (DEM). This assisted the analyst by creating a much more accurate geobiophysical model of the earth’s surface. This was accomplished as a result of simulating the moderate to high topographic relief found within the mountainous terrain environments of the Appalachian Mountain’s coalfields. Feature extraction was improved as well as visual interpretation. The research objective was to develop and evaluate new techniques for combining 3D Models with feature extraction processes and thereby creating more accurate thematic information classification maps. Improved techniques result from radiometric corrections, increased resolution, and data enhancement from the DEM’s. The method used incorporated the advantages of several software packages (ER Mapper, Surfer, and ArcView). These packages provide different image processing and geographic information system capability. Clusters were identified in ER Mapper with classification techniques for feature extraction. This was the process used to identify clusters of similar data in the frequency domain of an image that correlate to different vegetation, urban and/or rural areas. The research results show a substantial improvement in feature extraction and 3D-geobiophysical modeling

The Application of Advanced Technologies for Agriculture and Rangeland Management

This project demonstrates two applications of remote sensing in agricultural and rangeland environments. In the first, an unmanned aerial system (UAS) equipped with a multi-spectral sensor was used to estimate canopy cover across four different cover crop trials at four time periods. In the second, a local database of stationary camera trap images of wildlife was used to train a convolutional neural network to automatically catalogue images by identifying the animal in those images. Both projects aimed to provide an example of how remote sensing platforms and machine learning techniques can facilitate the rapid collection and processing of large-scale field data. In both projects, methods were developed that confirm the utility of advanced remote sensing and computer vision technologies

TESTING A COMBINED MULTISPECTRAL-MULTITEMPORAL APPROACH FOR GETTING CLOUDLESS IMAGERY FOR SENTINEL-2

Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved

Image Simulation in Remote Sensing

Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers

Earth Resources: A continuing bibliography, with indexes, issue 31

This bibliography lists 505 reports, articles, and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis