Multi-Scale Segmentation Method Based on Binary Merge Tree and Class Label Information

This paper presents a newly developed segmentation algorithm for high-resolution remote sensing imagery. The new method is based on binary merge tree (BMT) and mainly comprises two parts, which are BMT construction and tree node selection. The first part is initialized by super-pixels, which are iteratively merged through using the well-known multi-resolution segmentation approach to complete BMT construction. The primary contribution of this paper resides in the second part, in which a new tree node selection criterion is developed. The new criterion is formulated by using spectral variance and class label cues in order to better describe intra-segment homogeneity. To validate the proposed strategy, four multispectral scenes captured by two Chinese satellites, ZiYuan-3 and GaoFen-2, were adopted for segmentation experiments. The results indicated that our method out-performed some state-of-the-art algorithms in terms of segmentation accuracy.