We provide an experimental study of the channel characteristics in an underwater wireless optical communication (UWOC) system given by a 35-m transmission distance in various water types. A UWOC system using a low-power 488-nm laser diode is established to comprehensively evaluate the influence of the link distance, water turbidity, receiver parameters, and link misalignment on the communication link power. The results show that the link misalignment-induced power loss is significant and the effect of receiver parameters on power is limited. However, a higher turbidity or longer signal transmission distance can help to reduce the link misalignment effect and manifest the receiver parameters effect on the received power. In particular, in turbid waters with an attenuation coefficient of 0.471 m − 1, the change of the signal reception power curve is small over a certain degree of the link misalignment in the 35-m physical distance, and the reasonable configuration of receiver parameters effectively improves the signal reception quality in the UWOC system. These results can provide theoretical guidance for optimizing the UWOC system.
In the field of hyperspectral image (HSI) processing, shadow regions in HSIs are often ignored or simply treated as a category because of their low reflectivity and complex information. There have been some studies on shadow regions of HSIs but there are still few effective algorithms to detect the real substances under the shadow regions. In view of this problem and the good performance of convolution neural network (CNN) in HSI classification and target detection, this paper improved target detection method in shadow regions in HSI which combines the CNN and the adaptive coherence/cosine estimator (ACE) of the spectral derivative image. This method includes three main steps: firstly, shadow region would been determined by CNN model whose main parameters have been adjusted to optimize the network performance; secondly, the derivative data of hyperspectral image would be obtained by deriving the shadow region of hyperspectral image; finally, due to the prominent performance of ACE algorithm in target detection of HSI, this algorithm could be applied to detect the substances contained in the shadow regions. To assess the performance of the proposed method, one widely used HSI dataset is used in the experiments. The numbers of experiment results show that the proposed method can detect the substances under the shadow regions in HSIs and it also has promising prospect in the field of HSI data processing.
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