Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images

Yang, Jian; He, Yuhong; Caspersen, John P.

doi:10.1016/j.rse.2016.12.011

Cited by 68 publications

(48 citation statements)

References 49 publications

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“…By many experiments, the shape parameter is set as 0.1 and the compactness parameter is set as 0.5. As for JSGE, only the parameter "Nu" is chosen by experiments and other parameters are adopted as recommended in the original reference [6] since these parameters can obtain reliable results in various environments [7,8] as well as in our experiments. For SRM, to give a hierarchy of segmentations at different scales, a set of scale parameters (Q − level) are tuned according to the original reference [38], only the one which achieves the best segmentation is selected in this paper as listed in Table 2.…”

Section: Evaluation Methods and Metricsmentioning

confidence: 99%

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Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Sun

Hao²,

Chen³

et al. 2017

Remote Sensing

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Abstract:The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images.

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Section: Evaluation Methods and Metricsmentioning

confidence: 99%

“…In recent years, region-based methods, such as region merging, have become increasingly popular in the field of VHR remote sensing image segmentation [6]. Region merging is able to produce boundary-closed and spatial-continuous regions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Sun

Hao²,

Chen³

et al. 2017

Remote Sensing

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“…For instance, He et al and Wang et al optimized the MC to encompass both intra-segment homogeneity and inter-segment heterogeneity [27,28]. Yang et al proposed to design the MC based on only inter-segment homogeneity without the usage of heterogeneity characteristics [29]. In addition, boundary information has also been involved in the MC to support the image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, the segment-merging methods employing local spectral angle (SA) threshold as MC attract many scholars' attention [29]. Compared with the conventional strategies using global SA threshold, these kinds of methods refine the preset threshold with local homogeneity to obtain adaptive local SA threshold.…”

Section: Introductionmentioning

confidence: 99%

“…Then, we assess the spectral distance between adjacent regions through their SA and construct an initial MC based on the preset SA threshold. After that, based on the suggestion in related works [29], the preset SA threshold is refined by the local homogeneity of adjacent regions, which is quantified by the weighted average of inter-segment and boundary homogeneities in terms of relative areas of the boundary regions and internal regions, to obtain the adaptive local SA threshold. Finally, the underlying regions are merged when the minimum spectral distance is less than the adaptive local SA threshold.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Superpixel-based principal component analysis for high resolution remote sensing image classification

2019

Multimed Tools Appl

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Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images

Cited by 68 publications

References 49 publications

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

Dynamic Post-Earthquake Image Segmentation with an Adaptive Spectral-Spatial Descriptor

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

Superpixel-based principal component analysis for high resolution remote sensing image classification

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