In order to better characterize the polarization characteristics of reflected light in oil spill monitoring by remote sensing on the sea surface and improve the accuracy of oil spill detection, the quaternion method is introduced into the oil spill detection system, and a kind of oil spill monitoring technology based on quaternion is proposed in this paper. Firstly, the quaternion matrix method for calculating the sea surface reflection polarization information is proposed, and the relationship between the characteristic parameters of quaternion and the refractive index of oil film is discussed. Then, four kinds of oils crude oil, soybean oil, lubricating oil and diesel oil are used as the experimental samples for oily pollution on water surface, and the characteristic quaternion parameters, s and p components of different samples are obtained through the reflection images at different polarization directions, so the identification of different oil products is realized.. The experimental results show that the quaternion polarization characteristics can be used as an important parameter for oil identification.
The contact measurement techniques are typically used in the field of object material classification. It has a lot of disadvantages, such as the complex operation and time-consuming. In this paper, a new non-contact object material identification method based on Convolutional neural networks (CNNs) and polarization imaging is proposed. Firstly, the relationship between the complex refractive index of object and the polarization information is simulated, and then the structure of the CNNs is constructed according to the specific conditions of the polarization imaging system. The accuracy of the identification method is measured by repeated test using 7 materials. The experimental results show that the CNNs model can quickly realize the object material classification with the polarization images, and the classification accuracy is above 92%.
The edge method is one of the effective measurement methods based on image analysis for the Modulation Transfer Function of optical systems. By artificially changing the imaging environment, the corresponding changes of the MTF is obtained through the analysis of the object polarization images in the air and at different depth of water. The experimental results show that the MTF of the polarization optical system is stable when the water depth is within a certain range; when the water depth exceeds the threshold, the MTF of the system is drastically reduced. The change rule of MTF also varies with different materials.
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