Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.
Laser Raman spectroscopy is a powerful instrument commonly used for detection of bulk and trace amounts of explosives. The work carried out in this paper is divided into two phases; the first phase is to propose a real time standoff explosive detection and identification system based on Raman spectroscopy that can be deployed in static checkpoints. The measurement is performed for samples placed in contact and at distances up to 1 m in ambient light conditions. The second phase is to propose a novel sophisticated signal processing and pattern recognition techniques for accurate identification and classification of the investigated materials.
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