Multimedia Digital Signal Processing of Infrared Chemical Remote Sensing Based on Piecewise Linear Discriminant Algorithm

Meitao Gong

doi:10.1155/2021/6337431

Abstract

According to the basic principle of piecewise linear classifier and its application in the field of infrared chemical remote sensing monitoring, the characteristics of unilateral piecewise linear classifier applied to the infrared spectrum identification of chemical agents are studied. With the characteristic of separate transmission, the characteristic recovery with the total observed deviation is used for the model. The relaxation factors are used to replace the constrained conditions that cannot be optimized into constrained separate line segment calculation conditions. Experiments show that the result of signal recovery is better than traditional Wiener filtering and Richardson–Lucy methods.

Highlights

Sensors mainly show signals that are fused together with different factors, and the transmission path is more complicated
In this article, the restoration of infrared chemical remote sensing multimedia digital signals is proposed based on the boundary variation model, and at the same time, this model is equivalently segmented for calculation and judgment, and the original signal is appropriately restored [6]
In the successive inspection algorithm, the inequality B < ΛK < A related to the inspection statistics is always established within a certain inspection time, and there is possibility for no judgment

Summary

Introduction

Sensors mainly show signals that are fused together with different factors (noise and ineffective signals), and the transmission path is more complicated. Even if a sensor with a higher function is used, the signal results that can be obtained are not ideal. Deconvolution is a way to feed back the original signal based on the fused signal. We published a variation model of deformation and constraint [5]. Under this premise, in this article, the restoration of infrared chemical remote sensing multimedia digital signals is proposed based on the boundary variation model, and at the same time, this model is equivalently segmented for calculation and judgment, and the original signal is appropriately restored [6]

Result output

Feature Extraction of FrequencyDomain Signals

Simulation Experiment

Conclusions