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Remote Sensing, Free Full-Text

By A Mystery Man Writer

Use of landmines as a weapon of unconventional warfare rapidly increased in armed conflicts of the last century and some estimates suggest that at least 100 million remain in place across post-conflict nations. Among munitions and explosives of concern (MECs), aerially deployed plastic anti-personnel mines are particularly challenging in terms of their detection and subsequent disposal. Detection and identification of MECs largely relies on the geophysical principles of magnetometry and electromagnetic-induction (EMI), which makes non-magnetic plastic MECs particularly difficult to detect and extremely dangerous to clear. In a recent study we demonstrated the potential of time-lapse thermal-imaging technology to detect unique thermal signatures associated with plastic MECs. Here, we present the results of a series of field trials demonstrating the viability of low-cost unmanned aerial vehicles (UAVs) equipped with infrared cameras to detect and identify the most notorious plastic landmines—the Soviet-era PFM-1 aerially deployed antipersonnel mine. We present results of an experiment simulating analysis of a full-scale ballistic PFM-1 minefield and demonstrate our ability to accurately detect and identify all elements associated with this type of deployment. We report significantly reduced time and equipment costs associated with the use of a UAV-mounted infrared system and anticipate its utility to both the scientific and non-governmental organization (NGO) community.

Remote Sensing, Free Full-Text

Remote Sensing of Environment, Journal

Remote Sensing, Free Full-Text

Geoscience and Remote Sensing eNewsletter - GRSS-IEEE

Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

Remote Sensing, Free Full-Text

Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

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Remote Sensing, Free Full-Text

Remote Sensing, Free Full-Text

Remote Sensing, Free Full-Text

PDF) Remote Sensing Image Classification Using Transfer Learning Based Convolutional Neural Networks: An Experimental Overview