Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture
Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover ty...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-08-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/6/8/7339 |
| _version_ | 1819107518704517120 |
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| author | Jun Zhang Peijun Li Jinfei Wang |
| author_facet | Jun Zhang Peijun Li Jinfei Wang |
| author_sort | Jun Zhang |
| collection | DOAJ |
| description | Urban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is proposed. The multivariate textures are separately extracted from multispectral data using a multivariate variogram with different distance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. The multivariate textures and the spectral bands are then combined for urban built-up area extraction. Because the urban built-up area is the only target class, a one-class classifier, one-class support vector machine, is used. For comparison, the classical gray-level co-occurrence matrix (GLCM) is also used to extract image texture. The proposed method was evaluated using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Results demonstrated that the proposed method outperformed the use of spectral information alone and the joint use of the spectral information and the GLCM texture. In particular, the inclusion of multivariate variogram textures with spectral angle distance achieved the best results. The proposed method provides an effective way of extracting urban built-up areas from Landsat series images and could be applicable to other applications. |
| first_indexed | 2024-12-22T02:55:19Z |
| format | Article |
| id | doaj.art-6c33366a0a654047b9f07893f74ddf76 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-22T02:55:19Z |
| publishDate | 2014-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6c33366a0a654047b9f07893f74ddf762022-12-21T18:41:16ZengMDPI AGRemote Sensing2072-42922014-08-01687339735910.3390/rs6087339rs6087339Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate TextureJun Zhang0Peijun Li1Jinfei Wang2Institute of Remote Sensing and GIS, School of Earth and Space Sciences, and Beijing Key Lab of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, ChinaInstitute of Remote Sensing and GIS, School of Earth and Space Sciences, and Beijing Key Lab of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, ChinaDepartment of Geography, University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, CanadaUrban built-up area information is required by various applications. However, urban built-up area extraction using moderate resolution satellite data, such as Landsat series data, is still a challenging task due to significant intra-urban heterogeneity and spectral confusion with other land cover types. In this paper, a new method that combines spectral information and multivariate texture is proposed. The multivariate textures are separately extracted from multispectral data using a multivariate variogram with different distance measures, i.e., Euclidean, Mahalanobis and spectral angle distances. The multivariate textures and the spectral bands are then combined for urban built-up area extraction. Because the urban built-up area is the only target class, a one-class classifier, one-class support vector machine, is used. For comparison, the classical gray-level co-occurrence matrix (GLCM) is also used to extract image texture. The proposed method was evaluated using bi-temporal Landsat TM/ETM+ data of two megacity areas in China. Results demonstrated that the proposed method outperformed the use of spectral information alone and the joint use of the spectral information and the GLCM texture. In particular, the inclusion of multivariate variogram textures with spectral angle distance achieved the best results. The proposed method provides an effective way of extracting urban built-up areas from Landsat series images and could be applicable to other applications.http://www.mdpi.com/2072-4292/6/8/7339urban built-up areamultivariate textureOCSVMLandsat |
| spellingShingle | Jun Zhang Peijun Li Jinfei Wang Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture Remote Sensing urban built-up area multivariate texture OCSVM Landsat |
| title | Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture |
| title_full | Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture |
| title_fullStr | Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture |
| title_full_unstemmed | Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture |
| title_short | Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture |
| title_sort | urban built up area extraction from landsat tm etm images using spectral information and multivariate texture |
| topic | urban built-up area multivariate texture OCSVM Landsat |
| url | http://www.mdpi.com/2072-4292/6/8/7339 |
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