The four-optical coherent mixing detection technology can improve the dynamic range of moving target detection. This method has the difficulty of distinguishing the type of mixing output signal. We propose a method to distinguish the signal type by using the different peaks of the mixed signal spectrum. Based on the statistical theory, the power spectrum function of the mixed signal is obtained, and the numerical analysis of the influence of the light source line width and the light source frequency difference on the signal power spectrum is carried out. Through numerical calculation and analysis, the results show that the increase of the light source linewidth will lead to the broadening of the signal power spectrum. When the Doppler frequency difference is greater than 1/5 times the linewidth of the light source, the power spectrum of the two homodyne coherent signals in the four-light coherent mixing can be distinguished; when the Doppler frequency difference is less than 1/5 times the light source linewidth , The power spectrum of the two homodyne coherent signals in four-optical coherent mixing can not be distinguished.
Recently, lots of works try to capture contextual information to benefit semantic segmentation problems. However, most approaches adopt the uniform method to obtain context information, which means each pixel gets its context from the same region. We argue that for each pixel, contextual information aggregated from the region it belongs to can benefit the dense prediction, while those from other irrelevant regions possibly mislead the prediction. In this work, we propose a Region Context Module (RCM) that aggregates context for each pixel only from its object region. Furthermore, we design a Region Context Network (RCNet) embedded in the ASPP Module and Region Context Module. We conduct experiments on three datasets: Cityscapes, Vaihingen and Potsdam datasets. Extensive quantitative and qualitative evaluations demonstrate our model achieves favorable performance against state-of-the-art approaches.
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