A new segmentation method for very high resolution imagery using spectral and morphological information

Liu, Jing; Li, Peijun; Wang, Xue

doi:10.1016/j.isprsjprs.2014.11.009

Cited by 44 publications

(28 citation statements)

References 48 publications

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“…ED3 Modified had a stronger correlation with overall accuracy (0.83) than that of SEI (0.79), suggesting ED3 Modified was more related to the classification accuracy. The performance of ED3Modified and SEI for evaluating the CHM segmentation results were compared at a series of scale parameters (i.e., 10,20,30,40,50,60,70,80,90, and 100), through relating them to the corresponding overall accuracies when using both spectral and height information for object-based classification ( Figure 9). Since the higher overall accuracy indicates better classification while the lower ED3Modified or SEI indicates the better segmentation, the absolute value of correlation coefficient (|R|) was used to gauge the strength of the relationships.…”

Section: Resultsmentioning

confidence: 99%

“…The CHM was assigned a weight of 20% for both segmentation and classification while each layer of the multispectral image was weighted 20%, resulting in a total of 80% for the multispectral image. However, most of segmentation algorithms, for instance region merging [19,40,41] and watershed transformation [42][43][44], use single layer or weighted averages of multiple layers. If spectral layers introduce noise, the use of them could reduce segmentation quality, particularly in cases where they were highly weighted.…”

Section: Remote Sensing Data Processingmentioning

confidence: 99%

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Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

et al. 2015

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Abstract:Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28%˘6.16% vs. 68.54%˘9.03% and 64.51%˘11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

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Section: Resultsmentioning

confidence: 99%

Section: Remote Sensing Data Processingmentioning

confidence: 99%

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

et al. 2015

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“…The very high resolution (VHR) images, with a geometric positioning accuracy (spatial resolution) down to less than 1 m per pixel [3], such as those taken by IKONOS, Quickbird, GeoEye-1, Worldview-2 satellites, and aerial platforms, have been one of the most important sources of information required in timely disaster damage assessment. Such images have opened a door to a possibility of more objective and detailed damage description, however, efficient processing has long been a central issue [4,5]. Besides, the complex data properties in the form of heterogeneity and class imbalance in the post-earthquake VHR images constitute severe challenges for the segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Many researches developed the similarity measures from either spectral or spatial features [5,9], which often lead to incomplete description of the image contents [10]. This insufficient delineation is unable to fully capture the spatial and structural patterns of damage objects, and thereby degrades their applicability in the post-earthquake environment.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the testing order is static, which is usually specified at the very beginning and remains unchanged during the merging process. As a result, it is unable to adapt to the changes of merging regions and often results in over-segmentation (where some regions can be merged) [16] and under-segmentation (where some regions can be split) [5], especially for post-earthquake VHR images where the damage objects are distributed in random order and spatially scattered. Many researchers attempt to resolve these problems by introducing dynamic strategies, such as dynamic region merging (DRM) [17] and dynamic statistical region merging (DSRM) [15].…”

Section: Introductionmentioning

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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Classification of Post-earthquake High Resolution Image Using Adaptive Dynamic Region Merging and Gravitational Self-Organizing Maps

Zhang

Hao

Sun

et al. 2018

Springer Natural Hazards

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A new segmentation method for very high resolution imagery using spectral and morphological information

Cited by 44 publications

References 48 publications

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

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

Classification of Post-earthquake High Resolution Image Using Adaptive Dynamic Region Merging and Gravitational Self-Organizing Maps

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