随着遥感影像空间分辨率的提高, 地物纹理细节更加丰富, 采用传统的语义分割方法使分割结果过于细碎, 整体性不强.针对该问题, 提出一种改进迭代条件模型的遥感影像语义分割方法.首先采用L0梯度最小化模型对遥感影像去噪, 然后采用迭代条件模型, 通过更新影像中每个点的标记完成影像分割, 最后采用Kappa指数对实验结果进行精度评价.实验结果表明, 该方法是一种有效的遥感影像语义分割方法.%With the improvement of spatial resolution of remote sensing images, the details of ground texture are more abundant, and the traditional semantic segmentation method will make the segmentation result too fine and the integrity is not strong.To solve this problem, an improved iterative conditional model for semantic segmentation of remote sensing image is proposed.Firstly, the L0 gradient minimization model is used to denoise the remote sensing image, and then the image segmentation is completed by updating the mark of each point in the image by using the iterative conditional model.Finally, the accuracy of the experimental results is evaluated by the Kappa index.Experimental results show that the proposed method is an effective method for semantic segmentation of remote sensing image.
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