基于视觉注意的遥感图像森林植被纹理分割

Forest Vegetation Texture Segmentation in Remote Sensing Image Based on Visual Attention

树冠作为遥感图像森林植被的典型纹理单元,具有突出的结构纹理特征,但现有分割方法较少利用此类结构纹理进行分割。为此,提出一种基于视觉注意机制的遥感图像森林植被纹理分割方法。将遥感图像中树冠的形状和结构作为视觉注意目标,通过纹理滤波增强树冠纹理,使用特定的多尺度树冠显著图圆盘(SID)模型标记树冠,并将各个多尺度树冠SID作为种子,设计改进的区域生长方法分割森林植被区域。实验结果表明,该方法能够准确标记多数典型树冠,有效提高森林植被区域的分割精度。

As a typical texture unit of forest vegetation in remote sensing image,the canopy has prominent structural texture features,but the current segmentation methods make use of this texture insufficiently.Aiming at this problem,based on visual attention mechanism,a kind of forest vegetation remote sensing image texture segmentation method is proposed in this paper.The shape and structure of the canopy in the remote sensing image are used as the visual attention target,and the canopy texture is enhanced by the texture filtering method.The multi-scale canopy Salient Image Disk(SID)model is used to mark canopy and by taking the multi-scale canopy SID as seeds,improved region growing method is used to segment the area of forest vegetation.Experimental results show that the proposed method can accurately mark most typical canopies and improve the segmentation accurate of the forest vegetation.