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

Accurate remote sensing image segmentation can guide human activities well, but current image semantic segmentation methods cannot meet the high-precision semantic recognition requirements of complex images. In order to further improve the accuracy of remote sensing image semantic segmentation, this paper proposes a new image semantic segmentation method based on Generative Adversarial Network (GAN) and Fully Convolutional Neural Network (FCN). This method constructs a deep semantic segmentation network based on FCN, which can enhance the receptive field of the model. GAN is integrated into FCN semantic segmentation network to synthesize the global image feature information and then accurately segment the complex remote sensing image. Through experiments on a variety of datasets, it can be seen that the proposed method can meet the high-efficiency requirements of complex image semantic segmentation and has good semantic segmentation capabilities.

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Semisupervised Multiscale Generative Adversarial Network for Semantic Segmentation of Remote Sensing Image

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2020.3036823 ◽

2020 ◽

pp. 1-5

Author(s):

Jiaqi Wang ◽

Bing Liu ◽

Yong Zhou ◽

Jiaqi Zhao ◽

Shixiong Xia ◽

...

Keyword(s):

Remote Sensing ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Generative Adversarial Network ◽

Adversarial Network

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Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3033816 ◽

2020 ◽

pp. 1-17

Author(s):

Xin Pan ◽

Jian Zhao ◽

Jun Xu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Semantic Segmentation ◽

Training Sample ◽

Remote Sensing Images ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Sample Set

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Remote Sensing Image Dataset Expansion Based on Generative Adversarial Networks with Modified Shuffle Attention

Sensors ◽

10.3390/s21144867 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4867

Author(s):

Lu Chen ◽

Hongjun Wang ◽

Xianghao Meng

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Image Processing ◽

Remote Sensing Image ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Evaluation Indexes ◽

Adversarial Network ◽

Remote Sensing Image Processing ◽

Data Expansion

With the development of science and technology, neural networks, as an effective tool in image processing, play an important role in gradual remote-sensing image-processing. However, the training of neural networks requires a large sample database. Therefore, expanding datasets with limited samples has gradually become a research hotspot. The emergence of the generative adversarial network (GAN) provides new ideas for data expansion. Traditional GANs either require a large number of input data, or lack detail in the pictures generated. In this paper, we modify a shuffle attention network and introduce it into GAN to generate higher quality pictures with limited inputs. In addition, we improved the existing resize method and proposed an equal stretch resize method to solve the problem of image distortion caused by different input sizes. In the experiment, we also embed the newly proposed coordinate attention (CA) module into the backbone network as a control test. Qualitative indexes and six quantitative evaluation indexes were used to evaluate the experimental results, which show that, compared with other GANs used for picture generation, the modified Shuffle Attention GAN proposed in this paper can generate more refined and high-quality diversified aircraft pictures with more detailed features of the object under limited datasets.

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