基于改进分离阈值法的农作物遥感精细分类特征选择

doi:10.3969/j.issn.0255-8297.2019.01.006

Abstract

Abstract:

In this paper, we take full advantage of visible and infrared remote sensing images with high spatial resolution to improve classification processing efficiency. With these aims, we propose a method of feature bands selection combined with image entropy based on SEparability and THresholds (SEaTH) method. The proposed method can solve the problem that SEaTH takes no feature information into account. We apply random forest (RF) classifier in the selected features to finely classify crops of our experimental site. The proposed method is compared with those from the modified adaptive band selection (MABS) and density peaks clustering feature selection based on image entropy (IE-DPC). The comparisons illustrate that the proposed method can improve classification accuracy effectively and decrease the number of features. Meanwhile, our method can analyze quantitatively how the selected features affect identification of various land-covers.

Key words: aerial thermal infrared remote sensing image, SEparability and THresholds, random forest, image entropy, feature selection, precise classification

CLC Number:

P237

YANG Hui-wen, FANG Jun-yong, ZHAO Dong. Feature Selection in Precise Crop Classification Using Remote Sensing Data Based on Improved SEaTH Algorithm[J]. Journal of Applied Sciences, 2019, 37(1): 51-63.

References

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Feature Selection in Precise Crop Classification Using Remote Sensing Data Based on Improved SEaTH Algorithm

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