Large-area settlement pattern recognition from Landsat-8 data
The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan,...
| Main Authors: | , |
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| Format: | Journal article |
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Elsevier
2016
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| _version_ | 1826280913340727296 |
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| author | Wieland, M Pittore, M |
| author_facet | Wieland, M Pittore, M |
| author_sort | Wieland, M |
| collection | OXFORD |
| description | The study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties. |
| first_indexed | 2024-03-07T00:20:52Z |
| format | Journal article |
| id | oxford-uuid:7c7d0199-6930-4cc9-9328-4334bb1209df |
| institution | University of Oxford |
| last_indexed | 2024-03-07T00:20:52Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:7c7d0199-6930-4cc9-9328-4334bb1209df2022-03-26T20:57:23ZLarge-area settlement pattern recognition from Landsat-8 dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7c7d0199-6930-4cc9-9328-4334bb1209dfSymplectic Elements at OxfordElsevier2016Wieland, MPittore, MThe study presents an image processing and analysis pipeline that combines object-based image analysis with a Support Vector Machine to derive a multi-layered settlement product from Landsat-8 data over large areas. 43 image scenes are processed over large parts of Central Asia (Southern Kazakhstan, Kyrgyzstan, Tajikistan and Eastern Uzbekistan). The main tasks tackled by this work include built-up area identification, settlement type classification and urban structure types pattern recognition. Besides commonly used accuracy assessments of the resulting map products, thorough performance evaluations are carried out under varying conditions to tune algorithm parameters and assess their applicability for the given tasks. As part of this, several research questions are being addressed. In particular the influence of the improved spatial and spectral resolution of Landsat-8 on the SVM performance to identify built-up areas and urban structure types are evaluated. Also the influence of an extended feature space including digital elevation model features is tested for mountainous regions. Moreover, the spatial distribution of classification uncertainties is analyzed and compared to the heterogeneity of the building stock within the computational unit of the segments. The study concludes that the information content of Landsat-8 images is sufficient for the tested classification tasks and even detailed urban structures could be extracted with satisfying accuracy. Freely available ancillary settlement point location data could further improve the built-up area classification. Digital elevation features and pan-sharpening could, however, not significantly improve the classification results. The study highlights the importance of dynamically tuned classifier parameters, and underlines the use of Shannon entropy computed from the soft answers of the SVM as a valid measure of the spatial distribution of classification uncertainties. |
| spellingShingle | Wieland, M Pittore, M Large-area settlement pattern recognition from Landsat-8 data |
| title | Large-area settlement pattern recognition from Landsat-8 data |
| title_full | Large-area settlement pattern recognition from Landsat-8 data |
| title_fullStr | Large-area settlement pattern recognition from Landsat-8 data |
| title_full_unstemmed | Large-area settlement pattern recognition from Landsat-8 data |
| title_short | Large-area settlement pattern recognition from Landsat-8 data |
| title_sort | large area settlement pattern recognition from landsat 8 data |
| work_keys_str_mv | AT wielandm largeareasettlementpatternrecognitionfromlandsat8data AT pittorem largeareasettlementpatternrecognitionfromlandsat8data |