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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

Cited 6 time in wos
Cited 7 time in scopus
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Title
Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method
Other Titles
변환 영역 이미지 분해 방법을 이용한 SAR 해빙 이미지 고주파 복합 노이즈 프랙탈 분석 및 텍스처 분류
Authors
Shahrezaei, Iman Heidarpour
Kim, Hyun-cheol
Subject
Computer Science; Engineering; Telecommunications
Keywords
Discrete wavelet transform; fractal analysis; high-frequency multiplicative noise; raw data generation; synthetic aperture radar
Issue Date
2020-03
Citation
Shahrezaei, Iman Heidarpour, Kim, Hyun-cheol. 2020. "Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method". IEEE ACCESS, 8(1): 40198-40223.
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-ice spatial backscattering data generation and time-frequency domain (TFD) formulations from the perspective of uncorrelated HMN. To the best of our knowledge, this is the first time that the extraction of the resolution profile and raw data for the reference KOMPSAT-5 SAR sea-ice image have been derived, evaluated and compared both qualitatively and quantitatively at each scale of DWT decomposition on the basis of the presence of HMN. This paper also presents a novel detailed modeling of the multiresolution probability distribution function of the HMN and its power spectral density function modeling at each scale of the decomposition. Other quality assessment techniques, such as two K-means clustering algorithms and several visualized verification methods, such as gradient vector field, advection mapping and tensor field mapping, have been implemented in this regard to investigate embedded HMN suppression and its adverse effects on the presence of pixel anomalies. As a result, as the decomposition continues, the HMN at each scale of decomposition is constantly altering from high-frequency uncorrelated anomalies to low-frequency joint spatial information within the observed 2-D data. In other words, excessive multiscale HMN suppression will result in spatial information loss that makes the DWT scale selection quite important for texture classification. The results also show that the importance of HMN suppression in SAR sea-ice images in the form of pixel anomaly decomposition for the purpose of further texture investigation should be in accordance with the spectral behavior of the HMN. The results are helpful for SAR remote sensing image restoration and data preservation when dealing with high-resolution SAR images, such as in time series analysis, sea-ice texture change detection, and polar structural mapping. The proposed approach is implemented on real KOMPSAT-5 SAR satellite sea-ice images, while fractal spatial resolution profile simulations are carried out based on the inversed equalized hybrid domain image formation algorithm.
URI
https://repository.kopri.re.kr/handle/201206/11984
DOI
http://dx.doi.org/10.1109/ACCESS.2020.2976815
Appears in Collections  
2020-2020, Study on remote sensing for quantitative analysis of changes in the Arctic cryosphere (20-20) / Kim, Hyun-cheol (PE20080)
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