The realistic simulation of the cloud background has certain difficulty because of its appearance and irregular arbitrary distribution. In order to more realistically simulate cloud background, a multi-layer "cloud particle" superposition model is proposed in this paper based on the characteristics of real cloud background. First, characteristics of remote sensing of clouds under different distribution frequency are collected and the laws within them are analyzed. Second, the generation space of the "cloud particles" that will be generated is delimited based on the fractal theory. Third, cloud images at different frequency are generated based on the characteristic laws of the real cloud images. And finally, the multi-layer images are merged by linear superposition. The method used in this paper is also with controllable coverage, the remote sensing cloud images under different coverage, therefore, can be simulated.
To improve the detection rate of small target in infrared image, this paper proposes an infrared small target detection algorithm based on the fusion of multiple saliency information, which combines local contrast measure (LCM), curvature filtering and motion saliency. Firstly, three saliency maps of the infrared image are calculated separately to prepare for the next advantages integration. Then, to improve the contrast of the target, the LCM saliency map and curvature saliency map are filtered according to the motion saliency value. Finally, the fusion weight is determined by the background suppression factor of the saliency map so that the fusion saliency map is obtained. Experimental results show that the proposed infrared small target algorithm outperforms other comparing methods in terms of detection capability.
A focusing solution for bistatic forward-looking synthetic aperture radar (BFSAR) is presented. Forward-looking imaging is highly desirable in some potential applications, such as self-landing in bad weather, military surveillance, and navigation. Unfortunately, monostatic synthetic aperture radar reaches its limit when it is used in a forward-looking configuration. BFSAR can provide a high-resolution image in the forward-looking direction. However, due to the special forward-looking geometry, many proposed methods of deriving a bistatic point target reference spectrum (BPTRS) cannot handle the BFSAR data well. A modified Loffeld’s bistatic formula (MLBF) for a forward-looking configuration is proposed first; it can get an accurate BPTRS of BFSAR. Then, a chirp scaling algorithm (CSA) based on MLBF is derived. CSA without interpolation allows high performance. Numerical simulations show that the proposed focusing solution can handle the BFSAR data well and achieve a high-resolution focused image.
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