Hybrid region merging method for segmentation of high-resolution remote sensing images

Zhang, Xueliang; Xiao, Pengfeng; Xia, Feng; Wang, Jiangeng; Zuo, Wangmeng

doi:10.1016/j.isprsjprs.2014.09.011

Cited by 69 publications

(36 citation statements)

References 52 publications

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“…The proposed methodology was designed to work in detail with the images, which is accomplished via the generation of the region map, so that the evaluation of the regions takes place. The region growing is a segmentation method for defining objects accurately; in fact, similar SRG (Liu et al, 2015;Wang et al, 2010) and merging (Zhang et al, 2014) algorithms have been recently proposed that work well with VHR images. However, region growing is a rather time consuming procedure.…”

Section: Configuration and Deployment Aspectsmentioning

confidence: 99%

A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images

Fytsilis

Prokos

Koutroumbas

et al. 2016

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Configuration and Deployment Aspectsmentioning

confidence: 99%

A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images

Fytsilis

Prokos

Koutroumbas

et al. 2016

ISPRS Journal of Photogrammetry and Remote Sensing

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“…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%

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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“…With the developments in remote sensing technology, the spatial resolution of remote sensing imagery has improved from meter level to centimeter level [1][2][3]. Accurate segmentation of high-resolution remote sensing images plays an important role in obtaining detailed information of ground objects.…”

Section: Introductionmentioning

confidence: 99%

Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

Shi

Zhao

2020

Remote Sensing

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The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images.

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Hybrid region merging method for segmentation of high-resolution remote sensing images

Cited by 69 publications

References 52 publications

A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images

A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images

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

Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

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