Land Cover Classification in a Complex Urban-Rural Landscape with QuickBird Imagery

Abstract

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectralbased supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance.

Object Based Agricultural Land Cover Classification Map of Shadowed Areas From Aerial Image and Lidar Data Using Support Vector Machine

“…The spectral confusion among different land-covers, and the shadow problem often lead to poor classification (Lu et al, 2010). The spectral properties (mean and standard deviation) were extracted from the RGB (red, green and blue) bands of the shadow and non-shadow areas for various land cover types.…”

“…Therefore, proper selection of training sample plots is critical for land cover classification (Lu et al, 2010). Training and validation points for different classes including shadowed areas were selected homogeneously from the orthophoto.…”

Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery

“…Therefore, high spatial resolution imagery (better than 5 m) is necessary for accurate urban land-cover classification and has been extensively used in recent decades [14,15]. Compared to Landsat imagery with 30 m spatial resolution, high spatial resolution satellite imagery has its merits and shortcomings.…”

“…Compared to Landsat imagery with 30 m spatial resolution, high spatial resolution satellite imagery has its merits and shortcomings. For example, rich spatial information with clear shapes of different land covers is very suitable for visual interpretation [14], but produces high spectral variation for the same land cover such as building roofs, roads/streets and parking lots, and shadows from tall objects (e.g., tree crowns and buildings), resulting in difficulty in automatic land-cover classification [15]. Also, most high spatial resolution images only include visible and near-infrared (NIR) bands without shortwave infrared wavelengths, resulting in difficulty in classification of some land covers such as different forest types [4].…”

“…Many previous studies have indicated that pure spectral features in high spatial resolution images such as QuickBird cannot provide accurate land-cover classification using computer-based automatic classification approaches [14,15], but the incorporation of spatial information such as textural images into spectral bands or use of segmentation-based variables considerably improved the classification [15][16][17]. Also, pixel-based classification approaches such as maximum likelihood classifiers are not as good as object-based classification approaches for high spatial resolution images [15,[18][19][20][21]. Some machine-learning approaches such as support vector machine (SVM) and neural network that can handle high-dimensional data without assumption of normal data distribution have been proven to provide better classification performance than conventional statistical-based approaches [22,23].…”