4.7 Article

Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

期刊

REMOTE SENSING
卷 10, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/rs10010077

关键词

land cover mapping; mixed object; uncertainty; pixel-based classification; object-based classification; integration

资金

  1. National Natural Science Foundation of China [41701376, 41501453]
  2. Natural Science Foundation of Jiangsu Province [BK20170866]
  3. Key Program of Chinese Academy of Sciences [ZDRW-ZS-2016-6-3-4]
  4. Fundamental Research Funds for the Central Universities [2017B11714, 2016B11414]
  5. China Postdoctoral Science Foundation [2016M600356]
  6. State Key Laboratory of Resources and Environmental Information System, China

向作者/读者索取更多资源

Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method.

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