Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Chen, Bin; Qiu, Fang; Wu, Bingfang; Du, Hongyue

doi:10.3390/rs70505980

Cited by 46 publications

(25 citation statements)

References 34 publications

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“…In the process of minor object elimination, the area ratio T − rat, the minimum similarity T − sim, and the minimal objects area T − min are set to 20%, 0.15, and 150 as suggested in Ref. [35]. The FNEA is embedded in the commercial software eCognition [36,42] and ready to use for comparison, working on the same initial segmentations in ADRM.…”

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%

“…Likewise, cycle(R 4 , R 5 ) is constituted. Note that the global best [35] pair of regions must belong to the region pairs connected by cycle edges. Hence, it is a significant advantage to search among cycle edges for the global best pair since it can reduce the number of candidate pairs significantly.…”

Section: Dynamic Region Merging Based On Graph Modelsmentioning

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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“…When the spectral distance between adjacent regions was smaller than a metric, the adjacent segments were merged. Unlike other methods that use less intuitive metrics as MC [14,15,19], we employed an intuitive and physically-defined metric, SA as the MC. A preset SA threshold α was needed to determine if the spectral distance between two segments is close enough for them to be merged.…”

Section: Merging Criteriamentioning

confidence: 99%

“…Edge-based algorithms fracture the underlying images based on perceivable edges inferred by the dissimilarity between neighboring pixels [12]. However, edge-based algorithms are sensitive to noise or texture variation, thus apt to render over-segmentation around textured regions [14]. On the other hand, region-based algorithms exploit homogeneity or heterogeneity of adjacent regions to improve the robustness of the segmentation results against noise [13,15].…”

Section: Introductionmentioning

confidence: 99%

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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“…are in use for clustering high spatial resolution remote sensing images. Application of these approaches for clustering of images either leads to under-segmentation or over-segmentation [19,20]. Structural image indexing approach [21], semisupervised feature learning approach [22] and multi-scale manner using SVM approach [23] are also found fairly suitable in clustering high resolution images.…”

Section: Introductionmentioning

confidence: 99%

Clustering Techniques for Land Use Land Cover Classification of Remotely Sensed Images

Chakraborty¹

2020

Geographic Information Systems in Geospatial Intelligence

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Image processing is growing fast and persistently. The idea of remotely sensed image clustering is to categorize the image into meaningful land use land cover classes with respect to a particular application. Image clustering is a technique to group an image into units or categories that are homogeneous with respect to one or more characteristics. There are many algorithms and techniques that have been developed to solve image clustering problems, though, none of the method is a general solution. This chapter will highlight the various clustering techniques that bring together the current development on clustering and explores the potentiality of those techniques in extracting earth surface features information from high spatial resolution remotely sensed imageries. It also will provide an insight about the existing mathematical methods and its application to image clustering. Special emphasis will be given on Hölder exponent (HE) and Variance (VAR). HE and VAR are well-established techniques for texture analysis. This chapter will highlight about the Hölder exponent and variance-based clustering method for classifying land use/land cover in high spatial resolution remotely sensed images.

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Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Cited by 46 publications

References 34 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

Clustering Techniques for Land Use Land Cover Classification of Remotely Sensed Images

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