Abstract
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.
Highlights
Land cover is a fundamental variable in many scientific studies such as resource investigations, global climate change, and sustainable development [1,2,3]
It was necessary to analyze the impact of the tradeoff between the class proportion and the class spatial dependence of pixels on the performance of integration of pixel-based and object-based classifications (IPOC)
This study aimed to propose a new classification method through integration of pixel-based and object-based classifications (IPOC) for dealing with the mixed object uncertainty problem
Summary
Land cover is a fundamental variable in many scientific studies such as resource investigations, global climate change, and sustainable development [1,2,3]. The use of classifications is an efficient way to extract land cover information from remote sensing images [4,5]. Classification approaches can be divided into two general categories: (i) pixel-based classification, and (ii) object-based classification [6,7]. Pixel-based classification approaches use the pixel as the basic analysis unit while object-based classification approaches employ the object (i.e., a group of adjacent pixels) as the basic analysis unit [6]. Pixel-based classification contains mainly two types: (i) pixel-based hard classification, and (ii) pixel-based soft classification (PSC) ( termed as spectral unmixing) [7]. Pixel-based hard classification supposes each pixel is pure and it classifies individual pixels into
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