Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities

Abstract

Image segmentation is extensively used in remote sensing spectral image processing. Most of the existing region merging methods assess the heterogeneity or homogeneity using global or pre-defined parameters, which lack the flexibility to further improve the goodness-of-fit. Recently, the local spectral angle (SA) threshold was used to produce promising segmentation results. However, this method falls short of considering the inherent relationship between adjacent segments. In order to overcome this limitation, an adaptive SA thresholds methods, which combines the inter-segment and boundary homogeneities of adjacent segment pairs by their respective weights to refine predetermined SA threshold, is employed in a hybrid segmentation framework to enhance the image segmentation accuracy. The proposed method can effectively improve the segmentation accuracy with different kinds of reference objects compared to the conventional segmentation approaches based on the global SA and local SA thresholds. The results of the visual comparison also reveal that our method can match more accurately with reference polygons of varied sizes and types.

Local Scale-Guided Hierarchical Region Merging and Further Over- and Under-Segmentation Processing for Hybrid Remote Sensing Image Segmentation

“…The function of image segmentation is to divide a remote sensing image into spatially heterogeneous and spectrally homogeneous regions [20,21]. Most existent segmentation methods only consider the boundary information, such as the edge-based method [22][23][24][25], or the spatial information, such as the region-based method [26][27][28][29][30][31][32][33][34]. The edge-based method determines the edge for an image by tracking the pixel values that are discontinuous at different boundary regions [24], but it always tends towards over-segmentation.…”

“…Hence, an appropriate merging method is very critical for generating satisfying segmentation results. Following the existent literature, the merging criteria and merging order are the main focus in the merging algorithm design and optimization [29,30,34,[41][42][43]. However, the sizes of segments are controlled by scale or other segmentation parameters (SPs) in these merging algorithms.…”

Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

“…The sub-regions are generated by assigning pixels to the nearest seed points, where the dissimilarity measure between pixels and seed points is modeled by combing spatial and spectral distance with scale parameters. For the second issue, the general way is to cluster sub-regions with similar characteristics [33]. For example, Yang et al [34] proposed a local spectral angle as the similarity measure to merge the adjacent sub-regions.…”