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.

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。

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Experiment shows neural network classifier that is optimized by algorithm could not only have fast learning speed but also ensure accuracy of classification.

实验结果表明：算法优化后的神经网络分类器不但学习速度快，还能保证分类精度。

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The results show that a relatively satisfied classification result can be achieved by using the classification method combined with BP neural network in land cover classification.

结果表明地理辅助数据参与的BP神经网络用于土地覆盖分类研究可以获得相对较好的分类结果。

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