Abstract
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.
Highlights
Over the past three decades, urban areas in China have been expanding at an unprecedented pace, due to significant economic development
Multivariate variogram texture [31] is extracted from a Landsat TM/ETM+ multispectral image (Section 2.1), and the obtained multivariate texture is combined with the original multispectral image as an additional band
This paper proposed a method that combines multivariate variogram texture and spectral data in urban area classification
Summary
Over the past three decades, urban areas in China have been expanding at an unprecedented pace, due to significant economic development. ETM+) have been widely used for mapping urban extent and monitoring urban growth [2,3,4,5,6,7], due to the sensors’ capacity for synoptic view, repeat coverage over large areas and the availability of historical archive imagery [8]. Landsat sensors provide some advantages for the purposes of urban land mapping and change detection in terms of efficiency, as a single image can provide a synoptic view of an area of interest. In comparison to expensive higher-resolution sensors, the comparatively low-resolution nature of the Landsat TM/ETM+ sensor (30 m × 30 m) avoids complications from sparse coverage, limited scene availability and lack of data prior to 2000 when monitoring change for multiple periods. Complicating matters further, urban areas often display heterogeneous spectral characteristics and significant spectral confusion with other land cover classes: for example, barren land and asphalt concrete share similar spectral characteristics and, as a result, can be readily confused
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