Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

Tian, Liang; Zhong, Xiaorou; Chen, Ming

doi:10.1155/2021/9491376

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…is method involves obtaining labeled images and unlabeled images, which improves the accuracy of image semantic segmentation model. Tian et al [20] proposed a method to obtain the sample image of the enhanced image and fuse it. rough the fused image, the semantic segmentation image is obtained by using the semantic segmentation model.…”

Section: Relevant Research Workmentioning

confidence: 99%

A Method of Image Semantic Segmentation Based on PSPNet

Yang¹,

Guo²

2022

Mathematical Problems in Engineering

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Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set.

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Section: Relevant Research Workmentioning

confidence: 99%

A Method of Image Semantic Segmentation Based on PSPNet

Yang¹,

Guo²

2022

Mathematical Problems in Engineering

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“…In addition, it also allowed the gradient to spread directly toward any convolutional layer and the introduced attention mechanism obtained weighted high-level features, which was conducive to extracting global contexts and more effective semantic information, thereby better preserving the edges, texture, and other information of images and improving segmentation performance. For the ISPRS Potsdam dataset, the FPS of our method is 25, which is better than Tian et al (2021) and close to Chen et al (2020). It is shown that the proposed method can improve the segmentation accuracy while ensuring the computational efficiency.…”

Section: Isprs Potsdam Datasetmentioning

confidence: 81%

“…Using the method in Chen et al (2020), the OA was 0.871, the F1 was 0.883, and the MIoU was 0.671. Using the method in Tian et al (2021), the OA was 0.872, the F1 was 0.884, and the MIoU was 0.723. This showed that the proposed method not only integrated more original input information for this layer but also enhanced feature fusion by connecting the complete long-distance and short-distance residuals.…”

Section: Isprs Potsdam Datasetmentioning

confidence: 99%

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

Zhang,

Jiang

2023

International Journal on Semantic Web and Information Systems

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There are many problems with remote sensing images, such as large data scales, complex illumination conditions, occlusion, and dense targets. The existing semantic segmentation methods for remote sensing images are not accurate enough for small and irregular target segmentation results, and the edge extraction results are poor. The authors propose a remote sensing image segmentation method based on a DCNN and multiscale feature fusion. Firstly, an end-to-end remote sensing image segmentation model using complete residual connection and multiscale feature fusion was designed based on a deep convolutional encoder–decoder network. Secondly, weighted high-level features were obtained using an attention mechanism, which better preserved the edges, texture, and other information of remote sensing images. The experimental results on ISPRS Potsdam and Urban Drone datasets show that compared with the comparison methods, this method has better segmentation effect on small and irregular objects and achieves the best segmentation performance while ensuring the computation speed.

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“…et al, 2021;Seong and Choi, 2021). Later, Transformer-based and generative adversarial network (GAN) methods no doubt are getting more and more attention (Shamsolmoali et al, 2020;Tian et al, 2021;Cui L. et al, 2022;He et al, 2022;Wang L. et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based semantic segmentation of remote sensing images: a review

Shen

et al. 2023

Front. Ecol. Evol.

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Semantic segmentation is a fundamental but challenging problem of pixel-level remote sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of deep learning (DL) in the computer vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there remains a deficiency in the evaluation and advancement of semantic segmentation techniques for RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past 5 years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques and models based on the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, allowing them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.

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Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

Cited by 8 publications

References 24 publications

A Method of Image Semantic Segmentation Based on PSPNet

A Method of Image Semantic Segmentation Based on PSPNet

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

Deep learning-based semantic segmentation of remote sensing images: a review

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