LOGO: an efficient local and global data collection mechanism for remote underwater monitoring

The oceans play an important role in our daily life and they form the lungs of our planet. Subsequently, the world ocean provides so many benefits for humans and the planet including oxygen production, climate regulation, transportation, recreation, food, medicine, economic, etc. However, the oceans suffer nowadays from several challenges ranging from pollution to climate change and destruction of underwater habitat. Hence, the use of remote sensing technologies, like sensor networks and IoT, is becoming essential in order to continuously monitor the wide underwater areas and oceans. Unfortunately, the limited battery power constitutes one of the major challenges and limitations of such technologies. In this paper, we propose an efficient LOcal and GlObal data collection mechanism, called LOGO, that aims to conserve the energy in remote sensing applications. LOGO is based on the cluster scheme and works on two network stages: local and global. The local stage is at the sensor node and aims to reduce its data transmission by eliminating on-period and in-period data redundancies. The global stage is at the autonomous underwater vehicle (AUV) level and aims to minimize the data redundancy among neighboring nodes based on a spatial-temporal node correlation and Kempe’s graph techniques. The simulation results on real underwater data confirm that LOGO mechanism is less energy consumption with high data accuracy than the existing techniques.

THE USE OF DEEP LEARNING IN REMOTE SENSING FOR MAPPING IMPERVIOUS SURFACE: A REVIEW PAPER

In recent years, deep convolutional neural networks (CNNs) algorithms have demonstrated outstanding performance in a wide range of remote sensing applications, including image classification, image detection, and image segmentation. Urban development, as defined by urban expansion, mapping impervious surfaces, and built-up areas, is one of these fascinating issues. The goal of this research is to explore at and summarize the deep learning approaches used in urbanization. In addition, several of these methods are highlighted in order to provide a comprehensive overview and comprehension of them, as well as their pros and downsides.

Advances of Forensic Remote Sensing Applications in the Face of Transnational Organized Crime and Terrorism

This chapter aims to investigate advance and relevance of remote sensing in detecting the increasing transnational terrorist and crimes acts. This work should take into the widest definition of transnational crimes and terrorist activities and the link between. Geopolitics has created a favor climate for the setting of transnational crimes and terrorism at the Tunisian-Libyan borders. A possible future scenario is the fall of a military base with high technology arms in the hand of terrorist groups. Remote would be relevant by monitoring of terrorist mobility and their number evolution, arms quality and quantity within the base and the region, linked illegal activities funding terrorist groups (human trafficking from Africa to Europe, arms trade towards Mali, and smuggling).

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

Single-Pixel Imaging with Heralded Single Photons

Traditional remote sensing applications are often based on pulsed laser illumination with a narrow linewidth and characteristic repetition rate, which are not conducive to covert operation. Whatever methods are employed for covert sensing, a key requirement is for the probe light to be indistinguishable from background illumination. We present a method to perform single-pixel imaging that suppresses the effect of background light and hence improves the signal-to-noise ratio by using correlated photon-pairs produced via spontaneous parametric down conversion. One of the photons in the pair is used to illuminate the object whilst the other acts as a temporal reference, allowing the signal photons to be distinguished from background noise. This heralding method shows how the noise regime is key to producing higher contrast images.

A regional approach to ancient urban studies in Greece through multi-settlement geophysical survey

The systematic exploration of large archaeological sites in the Mediterranean has evolved considerably since the “big dig” excavations. Pedestrian field surveying and remote sensing applications, including satellite and airborne image analysis, are now practical and relatively cost-efficient methods of characterizing large and diachronically diverse landscapes on regional scales. However, the use of geophysical techniques as a means for exploring manifold archaeological contexts is still in its infancy. In this paper, we highlight the advantages of archaeological geophysics to conduct regional surveys in the Mediterranean. Through a multi-site geophysical fieldwork campaign to investigate the patterns and dynamics of ancient cities in Greece, we show how geophysics offer new opportunities for characterizing the spatial attributes and regional dynamics of urban landscapes, and, in doing so, we make an argument for its wider adoption on regional survey projects.