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Identification of Remote Sensing-Based Land Cover Types Combining Nearest-Neighbor Classification and SEaTH Algorithm

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Abstract

The development of spaceborne remote sensing has greatly facilitated the land cover mapping at various spatial scales. Classification accuracy, however, is usually affected by the heterogeneous spectra of different land cover types for medium–low-spatial-resolution images. The study is aimed at improving the classification accuracy at a city scale by proposing a hierarchical classification method. Time-series Landsat-5 and Landsat-8 Operational Land Imager remote sensing images of 4 years were used as the classified images. A total of six first-class land cover types were determined, namely woodland, grassland, cropland, wetland, artificial surface and others. The object-based image analysis was chosen over pixel-based approaches. More specifically, the nearest-neighbor (NN) classification and SEparability and THresholds (SEaTH) algorithm were combined to produce a hierarchical classification method (NN-SEaTH). SEaTH algorithm was first used to extract the wetland after performing image segmentation in eCognition Developer. Then, the non-wetland was further classified to vegetation and non-vegetation by using a normalized difference vegetation index image. Finally, the other types were then obtained using the NN classification. To validate the proposed method, the NN classifier and NN-SEaTH method were compared. The proposed technique is shown to increase the overall accuracy (OA) and kappa coefficient (k) for the 4 years. The OA and k are, respectively, 96.46% and 0.9231, 96.63% and 0.9269, 96.88% and 0.9394, 95.22% and 0.9239 that are much larger than 88.13% and 0.7503, 88.83% and 0.7660, 88.64% and 0.7630, 87.33% and 0.7371 derived from the NN approach. The study provides a reference for medium-resolution-based land cover mapping by a hierarchical classification.

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Acknowledgements

The project was supported by the National Natural Science Foundation of China (41601466, 61672032), the Youth Innovation Promotion Association CAS (2017085), Natural Science Research Project of Anhui Provincial Education Department (KJ2018A0009) and Anhui Provincial Science and Technology Project (17030701062).

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Authors and Affiliations

  1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, 230601, China

    Jinling Zhao & Yan Fang

  2. Department of Geology and Surveying and Mapping, Shanxi Institute of Energy, Jinzhong, 030600, China

    Mingmei Zhang

  3. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China

    Yingying Dong

  4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Yingying Dong

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  1. Jinling Zhao
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  2. Yan Fang
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Correspondence to Yingying Dong.

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Zhao, J., Fang, Y., Zhang, M. et al. Identification of Remote Sensing-Based Land Cover Types Combining Nearest-Neighbor Classification and SEaTH Algorithm. J Indian Soc Remote Sens 48, 1007–1020 (2020). https://doi.org/10.1007/s12524-020-01131-6

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  • DOI: https://doi.org/10.1007/s12524-020-01131-6

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