For reducing the segmentation error in remote sensing images of vegetation regions and for solving over-segmentation and under-segmentation of targets caused by various factors such as coverage and noise, an adaptive morphology combined with multiscale remote sensing image segmentation method for vegetation regions is proposed. First, general adaptive neighborhood (GAN) is used to construct dilation and corrosion operations, and GAN morphological opening and closing operations are derived. Then, a GAN morphological compound filter is constructed to fill the holes with insufficient vegetation coverage to reduce the interference of noise on the images. Finally, the remote sensing image of the vegetation region is segmented using the multiscale segmentation algorithm. The experimental results show that the proposed method can effectively avoid the phenomenon of under-segmentation and over-segmentation. Moreover, it can effectively segment the remote sensing images of vegetation areas with different coverage. Compared with the traditional multiscale segmentation method and traditional morphological and multiscale combined method, the proposed method has higher segmentation accuracy.