Vegetation is an important kind of objects in remote sensing image segmentation, and vegetation fine-grained segmentation generally has three targets, i.e., arbor, shrub, grass and moss according to the scale. In view of the problem that single level multi-classification method can't make full use of the different scales of the texture of vegetation target so as to achieve more accurate multi-classification, the authors proposed a hierarchical multi-scale remote sensing image vegetation segmentation method based on spectral histogram. First, the vegetation areas in remote sensing images were extracted with the normalized difference vegetation index(NDVI), and then the multiple binary classification algorithm was implemented in the region to achieve multi-classification operation. At each classification level, the advantage of the prior knowledge and texture scale was taken to select texture filtering parameters, the spectrum histogram of each sub-block image was extracted from the filtering result to express texture features so as to achieve the segmentation of a level. The experimental results show that the proposed method uses the prior knowledge and texture scale of vegetation target at all levels, so that the texture filter is made to enhance treatment more targeted, the spectrum histogram feature has much more degree of differentiation, and the accuracy of the vegetation fine-grained segmentation has been improved significantly.
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2017, Vol. 29 
