scholarly journals Experimental Evaluation of PSO Based Transfer Learning Method for Meteorological Visibility Estimation

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 828
Author(s):  
Wai Lun Lo ◽  
Henry Shu Hung Chung ◽  
Hong Fu
Keyword(s):  
Transfer Learning ◽  
Experimental Evaluation ◽  
Estimation Method ◽  
Image Features ◽  
Parameters Estimation ◽  
Image Feature ◽  
Support Vector ◽  
Learning Method ◽  
Feature Vectors ◽  
Visibility Estimation

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust.

scholarly journals Micro-Motion Dynamics and Shape Parameters Estimation Based on an Infrared Signature Model of Spatial Targets

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 755 ◽  
Author(s):  
Dongya Wu ◽  
Huanzhang Lu ◽  
Bendong Zhao ◽  
Junliang Liu ◽  
Ming Zhao
Keyword(s):  
Radiation Intensity ◽  
Infrared Radiation ◽  
Estimation Method ◽  
Optimization Techniques ◽  
Parameters Estimation ◽  
Geometrical Shape ◽  
Infrared Signature ◽  
Micro Motion ◽  
Periodic Fluctuations ◽  
Motion Dynamics

Infrared imaging is widely applied in the discrimination of spatial targets. Extracting distinguishable features from the infrared signature of spatial targets is an important premise for this task. When a target in outer space experiences micro-motion, it causes periodic fluctuations in the observed infrared radiation intensity signature. Periodic fluctuations can reflect some potential factors of the received data, such as structure, dynamics, etc., and provide possible ways to analyze the signature. The purpose of this paper is to estimate the micro-motion dynamics and geometry parameters from the observed infrared radiation intensity signature. To this end, we have studied the signal model of the infrared radiation intensity signature, conducted the geometry and micro-motion models of the target, and we proposed a joint parameter estimation method based on optimization techniques. After analyzing the estimation results, we testified that the parameters of micro-motion and geometrical shape of the spatial target can be effectively estimated by our estimation method.

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