2020
DOI: 10.1109/access.2020.2976815
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Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method

Abstract: Texture in synthetic aperture radar (SAR) images is a combination of the intrinsic texture of scene backscattering and the texture due to noncoherent high-frequency multiplicative noise (HMN) interactions that reflect erroneous information and lead to observation misinterpretation. The focus of this paper is the fractal analysis of KOMPSAT-5 SAR images of noncoherent sea-ice textures while being decomposed by discrete wavelet transform (DWT) processing. As a novel approach, fractal analysis relies on SAR sea-i… Show more

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Cited by 28 publications
(10 citation statements)
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“…SAR-based methods generally need sufficient textures in SAR images. However, sea ice textures have different electromagnetic interaction behaviors and gray levels, which may result in misinterpreted observations and imprecise SAR classifications [48].…”
Section: Comparison With Sar-based Methodsmentioning
confidence: 99%
“…SAR-based methods generally need sufficient textures in SAR images. However, sea ice textures have different electromagnetic interaction behaviors and gray levels, which may result in misinterpreted observations and imprecise SAR classifications [48].…”
Section: Comparison With Sar-based Methodsmentioning
confidence: 99%
“…According to the research of [25], not all the features of the stages were necessary to contribute to the decoder module. This motivated us to find a lightweight method to incorporate multi-level context into encoded features.…”
Section: Multi-level Fusion Decodermentioning
confidence: 99%
“…The existing single-branch segmentation model did not fully consider the feature information of different scales, and the existing multi-scale feature fusion model requires a lot of computation [23,24]. In order to quickly and accurately segment desert remote sensing images, it is still necessary to further strengthen the multi-scale information fusion effect [25], reduce the number of parameters, and speed up model convergence.…”
Section: Introductionmentioning
confidence: 99%
“…Wavelet-transformed images are better for medical disease diagnosis and land-use classification [30]. 2D discrete wavelet transform (DWT) produces high-frequency and low-frequency components uniquely characterizing the surface texture, which contributes significantly to identifying sea ice using remote-sensing images [31,32]. However, few studies have explored the potential of the 2D wavelet transform for lithological mapping using SAR data.…”
Section: Introductionmentioning
confidence: 99%