KEYWORDS: Polarization, Imaging systems, Stereoscopy, Polarizers, 3D modeling, 3D image reconstruction, 3D image processing, Reflection, Metalenses, 3D acquisition
In this paper, a monolithic polarizing metalens is used to extract multiple polarization information of the target at the same time. The i1maging receiver uses a general detector easily available on the market, which can obtain the intensity information image of multiple polarization components. The polarization measurement speed is fast, and the number of modules in the optical system is reduced The monocular 3D imaging system based on polarization-sensitive metalens collects diffuse reflected light from the surface of the target, and uses different polarimetric sub-images obtained by the metalens to carry out 3D reconstruction of the target.
Remote sensing image object detection is the primary task in the field of intelligent processing and has important practical application value. However, the current intelligent processing method of remote sensing images is difficult to meet the real-time requirements. In order to improve the effectiveness, real-time on-board processing of the collected images has become an important direction. This article compares several commonly used deep learning object detection algorithms, selects YOLO v3 for in-depth research and optimization, and transplants the debugged algorithm to the Cambrian 1H8 embedded edge intelligent computing platform for performance testing. Experiments show that the algorithm has high accuracy and the running speed basically meets the real-time requirements. It can be used to study and test the real-time processing performance of remote sensing images.
When the human visual system processes the optical image, it will use the salient features of the target and transfer learning to perform deep processing to improve the accuracy of target recognition.The vision system can quickly transfer the existing experience to interpret the target image information, so that humans can recognize the external environment. This mechanism of applying a priori knowledge for image processing of new targets is transfer learning.This image processing method can be applied to the field of high-resolution remote sensing image processing.This paper proposes to use the transfer learning of remote sensing images and utilize the VGG model to expand the data to improve the accuracy of remote sensing image recognition tasks.In terms of innovative methods, it is proposed to use the training samples processed by the style transfer algorithm as the input of the convolutional neural network classification model, which can guide the model to learn more prior knowledge information than the data.In the algorithm design, the network parameters such as the training input, network structure, matrix expansion method and convolution kernel scale of the traditional VGG network were adjusted and optimized.The recognition accuracy experiments were conducted using CIFAR10 and DOTA datasets, and Google Earth was used.The results of random remote sensing images are verified.
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