Investigation the Ateb-Gabor Filter in Biometric Security Systems

Nazarkevych, Mariya; Klyujnyk, Ivanna; Nazarkevych, Hanna

doi:10.1109/dsmp.2018.8478535

Cited by 10 publications

(1 citation statement)

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“…The 2D Gabor filter is a sinusoidal plane wave in the spatial domain that can conduct a frequency analysis of 2D information, so as to achieve texture information extraction, with characteristics of multidirectional and multiscale transformation [53]. The complex number of 2D Gabor filter kernel function is defined as follows [54][55][56]:…”

Section: The Gabor Filtermentioning

confidence: 99%

A Semantic Segmentation Method for Remote Sensing Images Based on the Swin Transformer Fusion Gabor Filter

2022

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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.

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