Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

Piretzidis, Dimitrios; Sideris, Michael G.

doi:10.1007/s00190-017-1010-5

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…In this paper, the hybrid segmentation method is a two-stage technique, and the aim of executing the watershed transformation is to produce primitive segments for subsequent merging, in which over-segmentation is considered to be a good starting point. Thus, a filter is not implemented to remove noise prior to segmentation, which is often done to avoid over-segmentation [46,47].…”

Section: Watershed Transformationmentioning

confidence: 99%

Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

Wang

Meng

et al. 2018

Remote Sensing

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Image segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within-and between-segment heterogeneity in the OHRH method.

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Section: Watershed Transformationmentioning

confidence: 99%