Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method
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...
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ftdoajarticles:oai:doaj.org/article:2b2f63987bba4ce28238fd76f73ca9f4 2023-05-15T18:17:25+02:00 Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method Iman Heidarpour Shahrezaei Hyun-Cheol Kim 2020-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2020.2976815 https://doaj.org/article/2b2f63987bba4ce28238fd76f73ca9f4 EN eng IEEE https://ieeexplore.ieee.org/document/9016014/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2020.2976815 https://doaj.org/article/2b2f63987bba4ce28238fd76f73ca9f4 IEEE Access, Vol 8, Pp 40198-40223 (2020) Discrete wavelet transform fractal analysis high-frequency multiplicative noise raw data generation synthetic aperture radar Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2020 ftdoajarticles https://doi.org/10.1109/ACCESS.2020.2976815 2022-12-31T10:25:53Z 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 ... Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles IEEE Access 8 40198 40223 |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Discrete wavelet transform fractal analysis high-frequency multiplicative noise raw data generation synthetic aperture radar Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
spellingShingle |
Discrete wavelet transform fractal analysis high-frequency multiplicative noise raw data generation synthetic aperture radar Electrical engineering. Electronics. Nuclear engineering TK1-9971 Iman Heidarpour Shahrezaei Hyun-Cheol Kim Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method |
topic_facet |
Discrete wavelet transform fractal analysis high-frequency multiplicative noise raw data generation synthetic aperture radar Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Iman Heidarpour Shahrezaei Hyun-Cheol Kim |
author_facet |
Iman Heidarpour Shahrezaei Hyun-Cheol Kim |
author_sort |
Iman Heidarpour Shahrezaei |
title |
Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method |
title_short |
Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method |
title_full |
Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method |
title_fullStr |
Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method |
title_full_unstemmed |
Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method |
title_sort |
fractal analysis and texture classification of high-frequency multiplicative noise in sar sea-ice images based on a transform- domain image decomposition method |
publisher |
IEEE |
publishDate |
2020 |
url |
https://doi.org/10.1109/ACCESS.2020.2976815 https://doaj.org/article/2b2f63987bba4ce28238fd76f73ca9f4 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
IEEE Access, Vol 8, Pp 40198-40223 (2020) |
op_relation |
https://ieeexplore.ieee.org/document/9016014/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2020.2976815 https://doaj.org/article/2b2f63987bba4ce28238fd76f73ca9f4 |
op_doi |
https://doi.org/10.1109/ACCESS.2020.2976815 |
container_title |
IEEE Access |
container_volume |
8 |
container_start_page |
40198 |
op_container_end_page |
40223 |
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1766191625580576768 |