Abstract: The morphological filters can suppress impulse noise or small image components/structures while preserving very important geometrical features such as edges. So, the morphological filters have been widely used in image preprocessing to remove the image noises and noise reduction is critical step for image segmentation. Morphological filters analyze the geometrical structure of image by locally comparing it with a predefined elementary shape called a structure element. Different scale image edges are detected by using several typical structure elements. Large amounts of experimental results demonstrate that the size of structure element have much dependence with image background. Therefore, many studies devote to the adaptive optimization of structure elements of morphological filters. However, the structure element of the same scale is traditionally adopted to establish a filter and remove noise from very high resolution satellite images prior to image segmentation. This method ignores the problem of inconsistencies between different land use types in the noise scale. In this paper, for the complicated background satellite imagery, a multi-scale morphological filtering method, which takes full advantage of the merits of large and small structure element by weighted strategy and combines them with the filtering results of multi-scale structure elements, is proposed based on morphological opening- and closing-reconstruction operations. To evaluate the multi-scale morphological filter for the image segmentation, three filtering approaches and segmentation accuracy assessment results are compared in this study. Qualitative and quantitative experimental results show that the proposed method can effectively solve over-segmentation and under-segmentation problem that result from improper scale of structure element. Compared with accuracy assessments of single scale and multi-scale morphological filters, the multi-scale morphological filter segmentation obtained higher accuracy than single scale filter segmentation, and is suitable for removing the multi-scale noise from very high resolution satellite images.