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Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification.

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Title: Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification.
Authors: Zhang, Aizhu1,2 aizhuzhang@upc.edu.cn, Zhang, Shuang1,2 s17010060@s.upc.edu.cn, Sun, Genyun1,2 B18010057@s.upc.edu.cnsungenyun@upc.edu.cn, Li, Feng1,3 1501060218@s.upc.edu.cn, Fu, Hang1,2 s17010069@upc.edu.cn, Zhao, Yunhua4 zhaoyunhua@qdkcy.com.cn, Huang, Hui1,2 1501060224@s.upc.edu.cn, Cheng, Ji1,2 sdwzj@upc.edu.cn, Wang, Zhenjie1,2
Source: Remote Sensing. May2019, Vol. 11 Issue 9, p998. 1p.
Subjects: Image analysis, Statistical methods in image analysis, Algorithms, Remote sensing, Voting
Abstract: The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral–Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral–spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral–spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral–spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Aizhu%22">Zhang, Aizhu</searchLink><relatesTo>1,2</relatesTo><i> aizhuzhang@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Shuang%22">Zhang, Shuang</searchLink><relatesTo>1,2</relatesTo><i> s17010060@s.upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Genyun%22">Sun, Genyun</searchLink><relatesTo>1,2</relatesTo><i> B18010057@s.upc.edu.cnsungenyun@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Feng%22">Li, Feng</searchLink><relatesTo>1,3</relatesTo><i> 1501060218@s.upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fu%2C+Hang%22">Fu, Hang</searchLink><relatesTo>1,2</relatesTo><i> s17010069@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yunhua%22">Zhao, Yunhua</searchLink><relatesTo>4</relatesTo><i> zhaoyunhua@qdkcy.com.cn</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Hui%22">Huang, Hui</searchLink><relatesTo>1,2</relatesTo><i> 1501060224@s.upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Ji%22">Cheng, Ji</searchLink><relatesTo>1,2</relatesTo><i> sdwzj@upc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhenjie%22">Wang, Zhenjie</searchLink><relatesTo>1,2</relatesTo>
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  Data: The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral–Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral–spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral–spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral–spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs11090998
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        Text: English
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      – SubjectFull: Image analysis
        Type: general
      – SubjectFull: Statistical methods in image analysis
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Voting
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      – TitleFull: Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification.
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            NameFull: Zhang, Aizhu
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            – D: 01
              M: 05
              Text: May2019
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              Y: 2019
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