Most previous target detection methods are based on the physical properties of visible-light polarization images, depending on different targets and backgrounds. However, this process is not only complicated but also vulnerable to environmental noises. A multimodal fusion detection network based on the multimodal deep neural network architecture is proposed in this research. The multimodal fusion detection network integrates the high-level semantic information of visible-light polarization image in crater detection. The network contains the base network, the fusion network, and the detection network. Each of the base networks outputs a corresponding feature figure of polarization image, fused by the fusion network later to output a final fused feature figure, which is input into the detection network to detect the target in the image. To learn target characteristics effectively and improve the accuracy of target detection, we select the base network by comparing between VGG and ResNet networks and adopt the strategy of model parameter pretraining. The experimental results demonstrate that the simulated crater detection performance of the proposed method is superior to the traditional and single-modal-based methods in that the extracted polarization characteristics are beneficial to target detection.
Artificial target has the characteristics of stronger polarization, which is found in the process of
researching polarization remote sensing. So the polarization characteristics of artificial target has become one of the
highlights in the research of the polarization remote sensing. Metal coating target is a kind of typical artificial target,
previous studies have found that metal coating target polarization reflection characteristics vary with the condition
of different observation angle and incidence angle, and the difference of polarization characteristics greatly impact
the derivation and accuracy of metal material surface. In order to research the characteristics of metal coating
surface bidirectional reflectance polarization, this measurement experiment set up four observation azimuth angle
and nine observation zenith angle .On the basis of the measured data by using the semi-empirical model for data
fitting, then get the data of metal coating target hemisphere space distribution of polarization reflection
characteristics . This paper analyzes the relationship between target surface polarization degree and observation
azimuth angle, observation zenith angle, which provides theory and data support for the derivation of typical
artificial target structure features.
The image quality of optical remote sensing satellite is affected by the atmosphere, thus the image needs to be corrected. Due to the spatial and temporal variability of atmospheric conditions, correction by using synchronous atmospheric parameters can effectively improve the remote sensing image quality. For this reason, a small light spaceborne instrument, the atmospheric synchronous correction device (airborne prototype), is developed by AIOFM of CAS(Anhui Institute of Optics and Fine Mechanics of Chinese Academy of Sciences). With this instrument, of which the detection mode is timing synchronization and spatial coverage, the atmospheric parameters consistent with the images to be corrected in time and space can be obtained, and then the correction is achieved by radiative transfer model. To verify the technical process and treatment effect of spaceborne atmospheric correction system, the first airborne experiment is designed and completed. The experiment is implemented by the "satellite-airborne-ground" synchronous measuring method. A high resolution(0.4 m) camera and the atmospheric correction device are equipped on the aircraft, which photograph the ground with the satellite observation over the top simultaneously. And aerosol optical depth (AOD) and columnar water vapor (CWV) in the imagery area are also acquired, which are used for the atmospheric correction for satellite and aerial images. Experimental results show that using the AOD and CWV of imagery area retrieved by the data obtained by the device to correct aviation and satellite images, can improve image definition and contrast by more than 30%, and increase MTF by more than 1 time, which means atmospheric correction for satellite images by using the data of spaceborne atmospheric synchronous correction device is accurate and effective.
The polarization image fusion enhancement is the method which generates the enhanced fusion image with the
redundancy and complementary between the polarization parameters images. The fusion polarization image has a higher
contrast and signal to noise ratio. The detail of image is better than the polarization parameters images. Based on the
analysis of polarization imaging principle, the method of the polarization image enhancement has been researched. First,
the polarization image fusion method which is based on the modulation in space domain has been researched and
developed. Second, the advantages and disadvantages of this method have been analyzed, and multi-scale analysis has
been introduced. A new polarization image fusion method which is based on modulation in multi-scale space has been
presented. The result of experiment shows that the fusion images can better characterize the polarization information of
different goals and scenarios. The result image can make the target detection, recognition, and other further processing
easier.
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