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.