Aiming at the demand for target detection of typical ground and object backgrounds in hyperspectral earth observation, this project solves the problems of insufficient sample size and low fidelity of target environment scene in intelligent learning, training and testing of a novel optoelectronic detection system, breaks through the key technologies of modeling of hyperspectral imaging simulation mechanism, modeling of hyperspectral reflectance/radiation characteristics of typical target background, computation of hyperspectral atmospheric transmission characteristics, and modeling of hyperspectral optical detectors, etc., and organically integrates remote sensing imaging and spectral measurement technologies. Through simulation, it simultaneously acquires spatial and spectral radiation information data from typical target backgrounds, thus providing foundational data support for the design of target detection algorithms based on hyperspectral data.
Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.
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