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Landsat Imagery Built-Up Area Extraction Method with Use of Multiple Indexes and Tasseled Cap Transformation.

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Title: Landsat Imagery Built-Up Area Extraction Method with Use of Multiple Indexes and Tasseled Cap Transformation.
Authors: Gu, Juan1 (AUTHOR), Dou, Peng2 (AUTHOR) doupeng@nieer.ac.cn, Huang, Chunlin3 (AUTHOR), Hou, Jinliang2,4 (AUTHOR), Zhang, Ying1,2 (AUTHOR), Han, Weixiao2 (AUTHOR), Guo, Jifu3,4 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1721. 23p.
Subjects: Principal components analysis, Landsat satellites, Geographic spatial analysis, Built environment
Abstract: Highlights: What are the main findings? A new built-up area extraction method based on PCA was proposed using a multi-band composite of NDBI, SAVI, MNDWI, and Tasseled Cap components; the second principal component (CorPC2 and CovPC2) significantly enhanced built-up features while effectively suppressing bare land interference. Both CorPC2 and CovPC2 methods produced typical twin peaks in gray histograms, enabling automatic threshold determination via an optimizing algorithm, and achieved higher producer's and user's accuracy compared to existing built-up indices. What are the implications of the main findings? The CovPC2 method offers higher automation by naturally suppressing bare land without requiring additional masks or manual correction, making it more practical for large-scale urban mapping. The proposed methods demonstrate strong anti-noise capability and superior separability between built-up areas and spectrally similar features (especially bare land), providing more regular, homogeneous, and reliable extraction results for urban development monitoring. Built-up area extraction is important for monitoring urban development and land-use change. Index-based methods are widely used for extracting built-up areas from Landsat imagery because of their simplicity and efficiency. However, conventional built-up indices often enhance bare land together with built-up areas due to their similar spectral characteristics, which reduces extraction accuracy and limits automatic threshold selection. To address this problem, this study proposes a built-up area extraction method based on multi-index synthesis and principal component analysis (PCA). First, NDBI (Normalization Differential Building Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), and the brightness, greenness, and wetness components of the Tasseled Cap transformation were stacked to construct a six-band synthetic index image, enhancing the contrast among built-up areas, bare land, vegetation, and water bodies. PCA was then applied to the synthetic image using both correlation and covariance matrices, and the second principal component was used to enhance built-up area information. The resulting CorPC2 and CovPC2 methods were evaluated and compared with conventional built-up indices. The results showed that both PC2-based methods improved the separability between built-up areas and background features, while CovPC2 achieved the best performance by more effectively suppressing bare-land interference without requiring an additional bare-land mask. In the main experimental area, CovPC2 achieved higher accuracy than the comparison methods, and its Otsu-based result remained close to the optimal-threshold result. Validation in three typical cities further demonstrated the applicability of the proposed method across different Landsat sensors and urban environments. The proposed PC2-based method, particularly CovPC2, provides an effective and more automated approach for Landsat-based built-up area extraction under bare-land interference. Additionally, by using a threshold optimizing algorithm, built-up areas can be automatically extracted with high accuracy. [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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  Data: Landsat Imagery Built-Up Area Extraction Method with Use of Multiple Indexes and Tasseled Cap Transformation.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1721. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+spatial+analysis%22">Geographic spatial analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Built+environment%22">Built environment</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Highlights: What are the main findings? A new built-up area extraction method based on PCA was proposed using a multi-band composite of NDBI, SAVI, MNDWI, and Tasseled Cap components; the second principal component (CorPC2 and CovPC2) significantly enhanced built-up features while effectively suppressing bare land interference. Both CorPC2 and CovPC2 methods produced typical twin peaks in gray histograms, enabling automatic threshold determination via an optimizing algorithm, and achieved higher producer's and user's accuracy compared to existing built-up indices. What are the implications of the main findings? The CovPC2 method offers higher automation by naturally suppressing bare land without requiring additional masks or manual correction, making it more practical for large-scale urban mapping. The proposed methods demonstrate strong anti-noise capability and superior separability between built-up areas and spectrally similar features (especially bare land), providing more regular, homogeneous, and reliable extraction results for urban development monitoring. Built-up area extraction is important for monitoring urban development and land-use change. Index-based methods are widely used for extracting built-up areas from Landsat imagery because of their simplicity and efficiency. However, conventional built-up indices often enhance bare land together with built-up areas due to their similar spectral characteristics, which reduces extraction accuracy and limits automatic threshold selection. To address this problem, this study proposes a built-up area extraction method based on multi-index synthesis and principal component analysis (PCA). First, NDBI (Normalization Differential Building Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), and the brightness, greenness, and wetness components of the Tasseled Cap transformation were stacked to construct a six-band synthetic index image, enhancing the contrast among built-up areas, bare land, vegetation, and water bodies. PCA was then applied to the synthetic image using both correlation and covariance matrices, and the second principal component was used to enhance built-up area information. The resulting CorPC2 and CovPC2 methods were evaluated and compared with conventional built-up indices. The results showed that both PC2-based methods improved the separability between built-up areas and background features, while CovPC2 achieved the best performance by more effectively suppressing bare-land interference without requiring an additional bare-land mask. In the main experimental area, CovPC2 achieved higher accuracy than the comparison methods, and its Otsu-based result remained close to the optimal-threshold result. Validation in three typical cities further demonstrated the applicability of the proposed method across different Landsat sensors and urban environments. The proposed PC2-based method, particularly CovPC2, provides an effective and more automated approach for Landsat-based built-up area extraction under bare-land interference. Additionally, by using a threshold optimizing algorithm, built-up areas can be automatically extracted with high accuracy. [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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      – Type: doi
        Value: 10.3390/rs18111721
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 23
        StartPage: 1721
    Subjects:
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Landsat satellites
        Type: general
      – SubjectFull: Geographic spatial analysis
        Type: general
      – SubjectFull: Built environment
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    Titles:
      – TitleFull: Landsat Imagery Built-Up Area Extraction Method with Use of Multiple Indexes and Tasseled Cap Transformation.
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            NameFull: Gu, Juan
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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