Spectral variability is one of the most limiting factors in hyperspectral unmixing, so it is important to further study the characteristics of spectral variability to improve the accuracy of unmixing. After conducting simulations under varying irradiation conditions, a linear mixed model combining endmember and band is proposed by introducing a band scaling factor to the endmember scaled spectrum. The total variation constraint is used to smooth the spatial distribution of both endmember and band scaling factors and then alternating iterative optimization is applied to solve the optimization problem. Experiments conducted with both simulated and real hyperspectral data sets indicate that the proposed algorithm is effective in hyperspectral unmixing and is superior to other state-of-the-art algorithms based on spectral variability.
The details and shape information of the target are effectively highlighted in the polarized image, which is more conducive to target detection. At present, the influence of different polarization parameters on the target detection task has not been studied in depth. There are problems that the ways of characterization of polarization parameter is so rich and varied that the polarization parameter is difficult to select, when we utilize the convolutional neural network (CNN) model to detect images obtained by polarimetric systems. In response to this problem, this paper uses the convolutional neural network (CNN) model to train a variety of polarized parametric images in many different input configuration for experimental comparison. Firstly, the sample data is acquired using a focal plane polarized camera, and the database is expanded using a data enhancement strategy to establish a polarized image data set. Then, different image input configurations are used as the training set, and the convolutional neural network (CNN) pre-training model is iteratively trained and fine-tuned to obtain the target detection model of the polarized image. Finally, in order to evaluate the performance of the model, the experimental trials are quantified by mean average precision (mAP) and processing time, and the influence of different polarization image input configurations on the detection model is analyzed. The experimental results show that compared with the model trained by single channel image configuration, the model trained by threechannel image configuration has better performance, but there is no obvious difference between models trained by different three-channel configurations.
Range-resolved laser reflective tomography is of great potential application in obtaining image information about an object with non-imaging laser radar system. The resulting time-dependent return signal which collected by non-imaging laser radar system provides one-dimensional raw projection data for reconstructing target image. However, this return signal can be regarded as the multi-convolution between the distribution function of target reflectivity, atmospheric transmission, detection circuit response and acquisition circuit response with the emitted laser pulse signal. In order to efficiently improve the reconstructed target image quality using short pulse laser, this paper presents a method used to restore the impulse response of target reflectivity modulation from the resulting time-dependent return signal so as to improve the reconstructed target image quality. This method is based on sending the laser and making it vertically irradiate to a profile of target in the front view to obtain basic wave pulse, then used it to recover the response of target reflectivity modulation. The experiment results show that this method is feasible and efficient.
The application of asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) in free-space optical (FSO) communication system can increase the system channel capacity, while the use of channel estimation technology can ensure that ACO-OFDM has better performance. Since the channel estimation performance is directly affected by the selection of pilot patterns, especially when using compressed sensing (CS), it has been a major task in OFDM transmission schemes. In order to improve the channel reconstruction accuracy, a hybrid optimization algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm is proposed for two types of pilot design criterion. Simulation results show that compared with other three algorithms, the proposed algorithm has faster convergence speed and lower convergence value. With the pilots designed by the proposed algorithm, the system can achieve higher channel reconstruction accuracy.
Proc. SPIE. 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018)
KEYWORDS: Visibility through fog, Signal attenuation, Air contamination, Telecommunications, Transmittance, Antennas, Atmospheric modeling, Systems modeling, Free space optical communications, Atmospheric optics
Considering the weather’s influence to atmospheric transmittance, the paper modifies the attenuation matrix in the basement of original MIMO FSOC system channel model. And then, based on the modified channel model, the capacity in different given conditions is shown through numerical simulation. The paper simulates the capacity in different transmitting antenna’s height as well as in different weather. Considering that in most conditions, the optical signal doesn’t transfer vertically, the paper stimulates the capacity versus system slant angle. The paper also simulates difference value of capacity. At last, the paper designs a software to evaluate whether the given conditions are fit for communication or not.