Abstract
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.
Highlights
Image segmentation technology is to divide the image into different types of uniform areas according to the internal characteristics of the image [1]
Based on the Generative Adversarial Network (GAN) and Fully Convolutional Neural Network (FCN) models, this paper proposes a new semantic segmentation method: (1) Aiming at the problem that complex remote sensing images are difficult to segment accurately, this paper builds a deep semantic segmentation network based on FCN, ensuring that the image receptive field is enlarged and realizing accurate semantic segmentation of remote sensing images
The speed is the number of pictures that can be processed per second. e accuracy indicators mainly include Pixel Accuracy (PA), Mean Pixel Accuracy, Intersection over Union (IoU), and Mean Intersection over Union (Mean Intersection over Union, mIoU)
Summary
Image segmentation technology is to divide the image into different types of uniform areas according to the internal characteristics of the image [1]. En, based on this semantic segmentation network, the efficient processing of remote sensing images can be realized. Many researchers have found that deep learning networks can extract features that are useful for image segmentation, so as to achieve accurate segmentation of remote sensing images [14]. In [16], facing the needs of massive remote sensing image processing, a remote sensing image extraction method based on U-net network was proposed to realize the semantic segmentation of high-resolution images. When the above image segmentation methods are used to segment complex remote sensing images, there is often too much element information in the image, which makes the image information extraction incomplete As a result, these methods are difficult to meet the needs of efficient semantic recognition.
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