Remote sensing image classification based on super pixel and peak density

doi:10.6046/gtzyyg.2020.04.06

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Abstract

In order to give full play to the advantages of hyperspectral spatial information and peak density clustering algorithm in dividing remote sensing image features, this paper proposes a hyperspectral image classification method based on the combination of hyperpixel and peak density features. Superpixel segmentation technology makes full use of the spatial and spectral information of hyperspectral images, dividing hyperspectral images into hyperpixels, extracting the gray value of hyperpixels as an important feature of peak density classification, selecting the spectrum with the highest peak density as the spectral cluster of the whole image, using the visual and hyperpixels as the basic units of classification, and then obtaining the pixels and hyperpixels respectively. The membership relation is obtained by the difference between spectral clusters. Finally, the image classification is completed by combining the membership degree. Experiments show that the proposed algorithm takes less time than other methods under the condition of ensuring the highest classification accuracy, and meets the requirements of hyperspectral image information extraction and analysis.

Keywords hyperspectral image peak density superpixel pixel feature fusion image classification

TP751

Issue Date: 23 December 2020

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	Ke SUN

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Ke SUN. Remote sensing image classification based on super pixel and peak density[J]. Remote Sensing for Land & Resources, 2020, 32(4): 41-45.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.06 OR https://www.gtzyyg.com/EN/Y2020/V32/I4/41

Fig.1 Operation steps of SLIC-DP algorithm

Fig.2 Hyperspectral image Pavia University data set

Fig.3 Classification results of Pavia University data set

Tab.1 Classification accuracy and calculation time of algorithm

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