提出了一种基于结构上下文的模糊神经网络(SCFNN)自动目标检测方法。模糊神经网络方法既具有神经网络的自适应性、并行性、鲁棒性、容错性、优化等优点，又集成了模糊集理论运用知识、规则描述解决系统不确定性的优点，因此成为图像处理和模式识别的一种强有力工具。使用模糊测度作为神经网络的目标函数可以有效地描述像素类别的不确定性，从而通过使其最小实现图像分类优化。对网络神经元加权过程进行结构上下文信息约束可以充分减小图像信息尤其是目标边缘等特性包含丰富信息的损失，有效地保持目标的轮廓和形状等属性，改善目标检测的误检率。针对目标遥感图像的实验，验证了SCFNN方法具有很好的自动目标检测能力，而相对于传统神经网络方法，具有有效的不确定性解决能力和更好的目标形状保持能力。

This paper proposes a structure-context based fuzzy neural network(SCFNN) approach for automatic target detection. Fuzzy neural network methods not only possess advantages as adaptivity, parallelism, robustness, ruggedness, and optimality, but integrate advantages as depicting and solving system uncertainty by knowledge and rules of fuzzy set theory. Accordingly, they are powerful tools for image processing and pattern recognition. Use fuzziness measures as objective function of neural network can depict uncertainty of pixels' category validly so as to optimize image classification by minimizing the objective function. Puting information constraint of structure context on neurons' weighting process can reduce loss of image information, especially, the rich information comprised by target edges, by which target's attributes such as profile and shape can be retained validly, and the false detection rate can also be improved prominently. Experiments on remotly sensed images of target are executed to validate SCFNN approach. The results exhibit that SCFNN possesses good ability to automatic target detection, simultaneously, possesses valid abilities to eliminating uncertainty and retaining target shape compared with conventional neural network methods.