Effects of Clouds and Shadows on the Use of Independent Component Analysis for Feature Extraction.
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| Title: | Effects of Clouds and Shadows on the Use of Independent Component Analysis for Feature Extraction. |
|---|---|
| Authors: | Bosques-Perez, Marcos A.1 (AUTHOR), Rishe, Naphtali1,2 (AUTHOR) rishen@cs.fiu.edu, Yan, Thony1 (AUTHOR), Deng, Liangdong2 (AUTHOR), Adjouadi, Malek1 (AUTHOR) |
| Source: | Remote Sensing. Aug2025, Vol. 17 Issue 15, p2632. 36p. |
| Subjects: | Independent component analysis, Landsat satellites, Signal separation, Multispectral imaging, Cloudiness, Feature extraction, Physics, Remote-sensing images |
| Abstract: | One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 187311700 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Effects of Clouds and Shadows on the Use of Independent Component Analysis for Feature Extraction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bosques-Perez%2C+Marcos+A%2E%22">Bosques-Perez, Marcos A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rishe%2C+Naphtali%22">Rishe, Naphtali</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> rishen@cs.fiu.edu</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Thony%22">Yan, Thony</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deng%2C+Liangdong%22">Deng, Liangdong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Adjouadi%2C+Malek%22">Adjouadi, Malek</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2025, Vol. 17 Issue 15, p2632. 36p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Independent+component+analysis%22">Independent component analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+separation%22">Signal separation</searchLink><br /><searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Cloudiness%22">Cloudiness</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17152632 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 36 StartPage: 2632 Subjects: – SubjectFull: Independent component analysis Type: general – SubjectFull: Landsat satellites Type: general – SubjectFull: Signal separation Type: general – SubjectFull: Multispectral imaging Type: general – SubjectFull: Cloudiness Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Physics Type: general – SubjectFull: Remote-sensing images Type: general Titles: – TitleFull: Effects of Clouds and Shadows on the Use of Independent Component Analysis for Feature Extraction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bosques-Perez, Marcos A. – PersonEntity: Name: NameFull: Rishe, Naphtali – PersonEntity: Name: NameFull: Yan, Thony – PersonEntity: Name: NameFull: Deng, Liangdong – PersonEntity: Name: NameFull: Adjouadi, Malek IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 15 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
