自适应多维特征减少的模糊C-均值遥感图像分割算法

针对传统分割算法难以对遥感图像进行有效分割的问题,提出了一种自适应特征减少的图像分割算法。首先对源图像进行超像素分割,将获得的超像素作为算法的基本操作对象。其次,提取图像的颜色、纹理、边缘以及空间等多维特征,并使用加权像素值来表示超像素的特征。再者,将模糊分离度量加入到FRFCM(feature-reduction fuzzy C-means)模型中,构造特征减少分割算法。该算法可以自动选择有用特征。最后对分割算法进行优化,获取最终分割结果。通过遥感图像分割实验表明,提出算法能有效分割遥感图像,在分割准确度、运行时间、消除噪声影响等性能方面优于其他同类算法。

Fuzzy C-means clustering with adaptive multiple features reduction for remote sensing image segmentation

The traditional algorithms have the low performance when applied to remote sensing images segmentation.Thus, this paper proposed a segmentation algorithm based on adaptive feature-reduction.Firstly, this algorithm divided the source image into super-pixels as the basic operation object.Then, it extracted the color, texture, edge and spatial features of images and used weighted pixel values to calculate the features of super-pixels.Furthermore, it added the fuzzy separation measure into the FRFCM model to construct own segmentation model.This model can automatically select useful features.Finally, this paper got the final segmentation result based on optimizing the segmentation model.Experiments on remote sensing images prove that this proposed algorithm has superior performance in terms of segmentation accuracy, running time and eliminate noise effects.