Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Huang, Xin; Zhang, Liangpei; Li, Pingxiang

doi:10.1109/lgrs.2006.890540

Cited by 162 publications

(69 citation statements)

References 12 publications

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“…This suggests that when using GE imagery for land use/cover mapping, more attention should be given to its spatial characteristics (such as shape, texture and context of objects) in the classification algorithm as these are well described in GE imagery. This conclusion agrees well with the study by Huang et al, which showed that the QB classification based on spatial features (i.e., shape index, length-width ratio) is significantly better than that based on spectral bands [45].…”

Section: Potentials Analysis Of Ge Imagery For Land Use/cover Mappingsupporting

confidence: 92%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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Section: Potentials Analysis Of Ge Imagery For Land Use/cover Mappingsupporting

confidence: 92%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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“…Various spectral-spatial classification methods have been proposed to extract urban land use maps. One popular method is to add into the original remote sensing spectral bands textural information derived from the original RSI, e.g., a grayscale co-occurrence matrix [13,14], morphological features [15], and a pixel shape index [16]. For example, Li et al used a Bayesian network to categorize building objects into different land use types based on their morphological, geometric, and contextual attributes [17].…”

Section: Introductionmentioning

confidence: 99%

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Jia

Ling

et al. 2018

Remote Sensing

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Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.

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“…[2] The tentative results of simulated and genuine datasets, demonstrate that the proposed methodology can effectively lessen the pseudo edges of the total variation regularization in the flat areas, and keep up the partial smoothness of the HR images. [6] More significantly with the comparison of the usual pixel based spatial data adaptive method, the method based on proposed region can do better i.e., in the super resolution process it helps in avoiding the effect of noise and maintains the robustness with changes in the intensity of noise.…”

Section: Proposed Methodsmentioning

confidence: 87%

Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization

Banafar¹,

Kourav²

2016

IJCA

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The method of total variation is used as a significant and competent image prior model in the regularization based area of image processing. However, as the model showing total variation supports a piecewise steady explanation, this process is classify below high intensity noise in the level areas of the picture is often poor, and a small number of pseudo edges are produced. Under this work we build up a spatially adaptive total variation model. Initially, we extract the spatial data based on each and every pixel, then two filtering process are combined to control the collision of pseudo edges. It also includes, the spatial information weight is build and classified with k-means clustering, and the cluster controls the center value of regularization strength in every region. The tentative results, of both simulated as well as genuine datasets, exhibit that the projected methodology can effectively diminish the pseudo edges formed by the total variation regularization in the flat areas, and maintain the limited smoothness of the HR images. The proposed region based spatial information adaptive variation model can effectively reduce the cause of noise on the spatial data extraction and maintain strength with changes in the noise intensity in the SR process as compare to traditional pixel based spatial information adaptive methodology.

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Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Cited by 162 publications

References 12 publications

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization

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