A per‐field classification method based on mixture distribution models and an application to Landsat Thematic Mapper data

Erol, Hamza; Akdeniz, Fikri

doi:10.1080/01431160512331326800

Cited by 28 publications

(14 citation statements)

References 14 publications

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“…However, when combined, the accuracy reaches 75%. Moreover, some function types that were not identified in previous studies have been classified in our result, such as service buildings, medical and public places [28,39]. Hence, the use of both features at the parcel level produced more detailed and accurate land use maps than studies using single source data [23,28].…”

Section: Discussioncontrasting

confidence: 38%

“…First, the entire study area was segmented into parcels based on road networks following the methods developed by Long and Liu [38] (Figure 2-1). Parcels are basic units used in this classification scheme with the assumption that they are homogeneous in terms of urban functions [39]. The parcels were then separated into built-up areas and non-built-up areas based on classified impervious surface areas [37] and defined our classification system based on these two regions (Figure 2-2).…”

Section: Methodsmentioning

confidence: 99%

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Mapping Urban Land Use by Using Landsat Images and Open Social Data

et al. 2016

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High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government.

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Section: Discussioncontrasting

confidence: 38%

Section: Methodsmentioning

confidence: 99%

Mapping Urban Land Use by Using Landsat Images and Open Social Data

et al. 2016

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“…A Landsat Thematic Mapper image of an agricultural area in the Seyhan plain (≈ 37 • N, 36 • E) in Adana having a size of 198 × 200 (in total 39,600) pixels was used as the multispectral image data [4]. The data were collected on 27 March 1992 (Path 175, Row 34).…”

Section: Methodsmentioning

confidence: 99%

“…The data were collected on 27 March 1992 (Path 175, Row 34). Landsat Thematic Mapper bands 3, 4 and 5 were used for the proposed per-field classification method since spectral radiance data from these three bands can be used to infer the properties related to pigment absorption, leaf structure and canopy leaf water content, respectively [4]. Landsat Thematic Mapper image and the fields in the Landsat Thematic Mapper image are shown in Figure 1 There are totally 269 fields in the agricultural area used for the proposed per-field classification method based on MDA.…”

Section: Methodsmentioning

confidence: 99%

A new per-field classification method using mixture discriminant analysis

Çaliş

Erol

2012

Journal of Applied Statistics

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In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.

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“…Per-field urban land use classification can overcome the limitations of per-pixel classification, and can incorporate field-only new attributes in the classification, such as the size, shape, and statistics of the field. Many studies have demonstrated improved classification results using per-field classification [9,10]. There are also classification approaches that integrate per-pixel and per-field methods; however, they have only been applied to land cover classification instead of urban land use classification [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

et al. 2017

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Abstract:In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping.

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A per‐field classification method based on mixture distribution models and an application to Landsat Thematic Mapper data

Cited by 28 publications

References 14 publications

Mapping Urban Land Use by Using Landsat Images and Open Social Data

Mapping Urban Land Use by Using Landsat Images and Open Social Data

A new per-field classification method using mixture discriminant analysis

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

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