An algorithm of fuzzy edge detection for wetland remote sensing image based on fuzzy theory

He, Dandan; Wang, Guan

doi:10.1007/s13204-021-02209-4

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The traditional image segmentation method is based on the color, shape, texture, and other features of the image, which leads to the image content being divided into different regions according to the edge features [ 4 , 5 , 6 ]. The classification results can be optimized to some extent by extracting geometric information from the image and combining it into class-by-class pixels [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images

Nan,

Li,

et al. 2024

Sensors

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In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet.

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Section: Introductionmentioning

confidence: 99%

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images

Nan,

Li,

et al. 2024

Sensors

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The Detection of Engineering Vehicle on Remote Sensing Image Using Improved YOLOv5

Chen,

Shi

et al. 2024

2024 5th International Conference on Big Data &Amp;amp; Artificial Intelligence &Amp;amp; Software Engineering (ICBASE)

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Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

Chen

Hua

2023

era

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<abstract><p>With the development of remote sensing technology, the resolution of remote sensing images is improving, and the presentation of geomorphic information is becoming more and more abundant, the difficulty of identifying and extracting edge information is also increasing. This paper demonstrates an algorithm to detect the edges of remote sensing images based on Grünwald–Letnikov fractional difference and Otsu threshold. First, a convolution difference mask with two parameters in four directions is constructed by using the definition of the Grünwald–Letnikov fractional derivative. Then, the mask is convolved with the gray image of the remote sensing image, and the edge detection image is obtained by binarization with Otsu threshold. Finally, the influence of two parameters and threshold values on detection results is discussed. Compared with the results of other detectors on the NWPU VHR-10 dataset, it is found that the algorithm not only has good visual effect but also shows good performance in quantitative evaluation indicators (binary graph similarity and edge pixel ratio).</p></abstract>

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An algorithm of fuzzy edge detection for wetland remote sensing image based on fuzzy theory

Cited by 3 publications

References 9 publications

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images

The Detection of Engineering Vehicle on Remote Sensing Image Using Improved YOLOv5

Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold

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