Forest Vegetation Remote Sensing Image Texture Segmentation By Visual Attention

With the improvement of the level of science and technology,using the data which is obtained from remote sensing images to extract features is one of the most effective technical measures.How to find the forest vegetation information accurately and efficiently in the remote sensing image has important research value in the fields of global ecosystem monitoring and analysis,land and resources management,urban construction and so on.Although forest vegetation texture,shape and spectral information is very rich,the structure of forest vegetation in remote sensing image is complex,and multi-scale space spread easily and finely and vulnerable area and problems such as the influence of the weather,leading to forest vegetation division harder.As the visual attention is widely used in remote sensing image segmentation in the forest,combined with the visual attention mechanism of remote sensing image forest vegetation segmentation method has a significant advantage.In the literature [28] proposed in the visual attention multi-scale SID(Salient Image Disk)model,relative to most of the other models can be more accurately segmented forest vegetation area.However,the remote sensing image exists similar to the crown shape Other easily confusing features,such as circular buildings and puddles,etc.,they can also be described by multi-scale SID.Therefore,on the basis of the above,this paper makes use of the structural texture of the canopy,and proposes a multi-scale canopy SID forest vegetation remote sensing image segmentation method based on the visual attention mechanism.The canopy serves as a typical texture unit for forest vegetation in remote sensing images,with prominent structural texture features.Firstly,the canopy was used as the visual attention target,and the canopy texture information of the forest vegetation area was enhanced by the bilateral filtering and Gaussian Laplacian filtering method,and the texture expression ability of the crown SID model was enhanced.Secondly,the texture description of the canopy is added to the traditional multi-scale SID model,that is,adding the gray difference texture information in both directions to the original SID model to establish the crown SID model,which can effectively eliminate the confused features compared with the traditional method.A typical marker of a typical crown.The results show that the method can effectively eliminate the large number of crowns in the confused terrain,and the canopy segmentation in the forest vegetation area is high.Finally,through the improved regional growth algorithm,the multi-scale canopy SID was used as seed to grow the forest vegetation area,and the segmentation of forest vegetation was well completed.Further research on the division of forest vegetation is to look for the SID in the region tosolve the problem of uneven distribution of the crown SID.The image is divided into several small areas,such as to decrease the visual attention operator no SID region constraints,including contrast,homogeneity,the mean gray level difference and two direction.