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Farmland parcel boundary extraction based on local feature extraction and sparse representation from remote sensing images

文献类型： 外文期刊

作者： Zheng, Mingxue 1 ; Chen, Pingting 1 ; Shen, Xiangcheng 1 ; Luo, Hongmei 1 ; Luo, Zhiqing 1 ; Ma, Hairong 1 ; Huang, Fenghua 2 ;

作者机构： 1.Hubei Acad Agr Sci, Inst Agr Econ & Technol, Wuhan, Peoples R China

2.Yango Univ, Fujian Key Lab Spatial Informat Percept & Intellig, Fujian, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Agr Monitoring & Early Warning Technol, Beijing, Peoples R China

4.Hubei Acad Agr Sci, Inst Agr Econ & Technol, Wuhan 430064, Peoples R China

5.Yango Univ, Fujian Key Lab Spatial Informat Percept & Intellig, Fujian 350015, Peoples R China

关键词： Feature extraction; sparse representation; farmland parcel boundary extraction; remote sensing images; agricultural application

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.4； 五年影响因子：3.6 ）

ISSN： 0143-1161

年卷期： 2024 年 45 卷 7 期

页码：

收录情况： SCI

摘要： Accurate information on agricultural field boundaries is important for precision agriculture. Contour detection combining local cues presents a high performance on nature images. Image sparse representation describes an image is reconstructed by using as few basic functions as possible. The number of farmland parcel boundaries is small and unbalanced for the whole agricultural fields. It fits the application category of sparse representation. In this research, we investigate an approach based on contour detection and sparse representation for the extraction of farmland parcel boundaries. First, field boundaries have an obvious brightness contrast with the internal parts of the farmland parcels. We capture the cue to describe per pixel. Then, we use efficient sparse coding algorithm to represent every pixel for boundary determination. Experimental results show that the proposed method achieves a sensitivity, specificity, accuracy, ${F_1}$F1and AUC of 0.6089, 0.9055, 0.8865, 0.4073 and 0.7552, respectively. The purpose of this paper is to demonstrate the potential of combining local features with sparse representation for a fast and accurate farmland parcel boundary extraction approach from remote sensing images. Comparison results with existing methods on two datasets demonstrate that the proposed method is able for accurate discrimination of the farmland parcel boundaries.

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