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空中侦察图像(以下称照片)能够如实反映目标的性质和状况,通过人工判读,利用人的高层感知融合侦察照片中目标、背景的形状、大小、色调、阴影、位置和活动等低层特征,能够准确评估目标伪装效果。由于航天侦察照片的判读是以航空侦察照片判读为基础,所以只需研究航空照片判读即可。如何快速、准确的判读航空照片,进行伪装目标效果评估,是国内外伪装技术发展的热点之一。目前,“人机交互”的判读模式占据主导地位,随着计算机技术的高速发展,它将逐步向自动化、智能化“模式识别”方向发展。论文将神经网络理论应用于光学伪装效果评估,探讨了一种基于BP神经网络模型的光学伪装效果评估模型。
Aerial reconnaissance images (hereinafter referred to as photos) accurately reflect the nature and condition of the target. By using artificial interpretation, the upper-level people perceive low-level features such as the shape, size, tone, shadow, position and activity of the target in the reconnaissance scout photograph Accurate assessment of the target camouflage effect. Since interpretation of aerospace reconnaissance photos is based on aerial reconnaissance photographs, it is only necessary to study interpretation of aerial photographs. How to quickly and accurately interpret aeronautical photographs and evaluate the effect of camouflage targets is one of the hot spots in the development of camouflage technologies at home and abroad. At present, the mode of interpretation of “human-computer interaction ” occupies a dominant position. With the rapid development of computer technology, it will gradually develop in the direction of automation, intelligent “pattern recognition ”. The thesis applies neural network theory to evaluate the optical camouflage effect, and discusses an optical camouflage effect evaluation model based on BP neural network model.