Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.
Micro unmanned aerial vehicle, mostly powered by electricity, plays an important role in many military and civil
applications, e.g. military detection, communication relay et al. But restricted endurance ability severely limits its
applications. To solve the problem, laser wireless power transmission system is proposed. However, overall efficiency of
the system is quite low. This paper describes basic structure of laser wireless power transmission system and its working
process. The system consists of two major modules: a high power laser source transmitting energy and a photovoltaic
receiver converting optical energy into electricity. Then factors influencing efficiency of the system are analyzed. It
suggests that electro-optical efficiency of laser, atmospheric impact on laser beam and photo-electric efficiency of
photovoltaic receiver play significant role in overall efficiency of the system. Atmospheric impact on laser beam mostly
derived from refraction, absorption, scattering and turbulence effects, leads to drop in energy and quality of laser beam.
Efficiency of photovoltaic receiver is affected by photovoltaic materials. In addition, matching degree between intensity
distribution of laser beam and layout of photovoltaic receiver also obviously influence efficiency of photovoltaic receiver.
Experiment results suggest that under non-uniform laser beam illumination, efficiency of photovoltaic receiver mostly
depends on layout of photovoltaic receiver. Through optimizing the layout of photovoltaic receiver based on intensity
distribution of laser beam, output power is significantly improved. The analysis may help to take corresponding measures
to alleviate negative effects of these factors and improve performance of laser wireless power transmission system.
Detecting enemy’s targets and being undetectable play increasingly important roles in modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. As supervised classification requires prior knowledge which cannot be acquired easily, unsupervised classification usually is adopted to process hyperspectral images to detect camouflaged target. But one of its drawbacks—low detecting accuracy confines its application for camouflaged target detecting. Most research on the processing of hyperspectral image tends to focus exclusively on spectral domain and ignores spatial domain. However current hyperspectral image provides high spatial resolution which contains useful information for camouflaged target detecting. A new method combining spectral and spatial information is proposed to increase the detecting accuracy using unsupervised classification. The method has two steps. In the first step, a traditional unsupervised classifier (i.e. K-MEANS, ISODATA) is adopted to classify the hyperspectral image to acquire basic classifications or clusters. During the second step, a 3×3 model and spectral angle mapping are utilized to test the spatial character of the hyperspectral image. The spatial character is defined as spatial homogeneity and calculated by spectral angle mapping. Theory analysis and experiment shows the method is reasonable and efficient. Camouflaged targets are extracted from the background and different camouflaged targets are also recognized. And the proposed algorithm outperforms K-MEANS in terms of detecting accuracy, robustness and edge’s distinction. This paper demonstrates the new method is meaningful to camouflaged targets detecting.