Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information

Li, Mu-Qing; Xu, Liyun; Gao, Shan; Xu, Na; Yan, Baixing

doi:10.3390/s19102385

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…One of the approach to improve the performance of FCM algorithm is making it self adaptive, where it can combine the neighborhood and non-neighborhood information of the image. The corresponding weight has to be calculated adaptively along with the addition of neighborhood spatial information to the clustering model [23]. The concept of markov random fields (MRF) is very much useful to make the FCM algorithm adaptive to the neighborhood systems [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic segmentation of plantar thermograms using adaptive C means technique

Prabhu

Verma

2021

IJECE

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Diabetic foot ulcer (DFU) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. The DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out highlights that, thermography is the most useful technique to measure the changes in the temperature of plantar surface and alerts to indicate the risk associated with DFU. The distribution of temperature does not have a fixed pattern across the patients, hence it makes the difficulty in measuring the appropriate changes. This gap will provide a scope to improve the analysis technique so as to measure the plantar surface temperature effectively and identify any abnormal changes. In this paper, the segmentation algorithm namely adaptive C means (ACM) for the image segmentation is discussed. ACM is based on the spatial information and this method includes the two stage implementation. In the first stage, nonlocal spatial information is added and in the second stage, spatial shape information is used in order to refine the constraint of local spatial. Outcome of the proposed method shows that ACM is very much effective and it outperforms the other existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic segmentation of plantar thermograms using adaptive C means technique

Prabhu

Verma

2021

IJECE

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“…The new method, designed for NPP-VIIRS NTL data, assigned a label to a pixel-based on the light intensity of the pixel and the pixels that are spatially close to it. Clustering is an unsupervised learning method widely used for classification and segmentation of remote sensing images, which was used in this study [32]. Twenty-five cities with different levels of the natural environment and economic development in China were selected as evaluation areas.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Urban Extent Extraction Method from NPP-VIIRS Nighttime Light Data

Chen

Zhang

et al. 2020

Remote Sensing

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An accelerating trend of global urbanization accompanying various environmental and urban issues makes frequently urban mapping. Nighttime light data (NTL) has shown great advantages in urban mapping at regional and global scales over long time series because of its appropriate spatial and temporal resolution, free access, and global coverage. However, the existing urban extent extraction methods based on nighttime light data rely on auxiliary data and training samples, which require labor and time for data preparation, leading to the difficulty to extract urban extent at a large scale. This study seeks to develop an unsupervised method to extract urban extent from nighttime light data rapidly and accurately without ancillary data. The clustering algorithm is applied to segment urban areas from the background and multi-scale spatial context constraints are utilized to reduce errors arising from the low brightness areas and increase detail information in urban edge district. Firstly, the urban edge district is detected using spatial context constrained clustering, and the NTL image is divided into urban interior district, urban edge district and non-urban interior district. Secondly, the urban edge pixels are classified by an adaptive direction filtering clustering. Finally, the full urban extent is obtained by merging the urban inner pixels and the urban pixels in urban edge district. The proposed method was validated using the urban extents of 25 Chinese cities, obtained by Landsat8 images and compared with two common methods, the local-optimized threshold method (LOT) and the integrated night light, normalized vegetation index, and surface temperature support vector machine classification method (INNL-SVM). The Kappa coefficient ranged from 0.687 to 0.829 with an average of 0.7686 (1.80% higher than LOT and 4.88% higher than INNL-SVM). The results in this study show that the proposed method is a reliable and efficient method for extracting urban extent with high accuracy and simple operation. These imply the significant potential for urban mapping and urban expansion research at regional and global scales automatically and accurately.

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Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information

Wu,

2024

Circuits Syst Signal Process

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Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information

Cited by 5 publications

References 30 publications

Automatic segmentation of plantar thermograms using adaptive C means technique

Automatic segmentation of plantar thermograms using adaptive C means technique

An Unsupervised Urban Extent Extraction Method from NPP-VIIRS Nighttime Light Data

Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information

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