Semantic segmentation of remote sensing images is increasingly important in urban planning, autonomous driving, disaster monitoring, and land cover classification. With the development of high-resolution remote sensing satellite technology, multilevel, large-scale, and high-precision segmentation has become the focus of current research. High-resolution remote sensing images have high intraclass diversity and low interclass separability, which pose challenges to the precision of the detailed representation of multiscale information. In this paper, a semantic segmentation method for remote sensing images based on Swin Transformer fusion with a Gabor filter is proposed. First, a Swin Transformer is used as the backbone network to extract image information at different levels. Then, the texture and edge features of the input image are extracted with a Gabor filter, and the multilevel features are merged by introducing a feature aggregation module (FAM) and an attentional embedding module (AEM). Finally, the segmentation result is optimized with the fully connected conditional random field (FC-CRF). Our proposed method, called Swin-S-GF, its mean Intersection over Union (mIoU) scored 80.14%, 66.50%, and 70.61% on the large-scale classification set, the fine land-cover classification set, and the "AI + Remote Sensing imaging dataset" (AI+RS dataset), respectively. Compared with DeepLabV3, mIoU increased by 0.67%, 3.43%, and 3.80%, respectively. Therefore, we believe that this model provides a good tool for the semantic segmentation of high-precision remote sensing images.
The echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.
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