数字农科院2.0

An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems

文献类型： 外文期刊

作者： Zhiwen Cai;Qiong Hu;Xinyu Zhang;Jingya Yang;Haodong Wei;Zhen He;Qian Song;Cong Wang;Gaofei Yin;Baodong Xu

作者机构：

关键词： cropland parcel extraction;image segmentation;optimal segmentation scale;random forest;region merging

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2022 年 14.0 卷 13 期

页码：

摘要： Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (Fab) and object-based segmentation evaluation (Fob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (Fab = 0.857 and Fob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extracted cropland parcels. These results indicate the great potential of using MSAOS for image segmentation in conjunction with random forest classification to effectively extract cropland parcels in smallholder farming systems.

分类号：

  • 相关文献

[1]Robust Image Segmentation Method For C.otton Leaf Under Natural C onditions Based On Immune Algorithm And Pcnn Algorithm. Kong, Fantao,Zhai, Zhifen,Wu, Jianzhai,Zhang, Jianhua,Han, Shuqing. 2018

[2]Image Segmentation to HSI Model Based on Improved Particle Swarm Optimization. Zhao, Bo,Mao, Wenhua,Zhang, Xiaochao,Chen, Yajun. 2009

[3]A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Ma, Juncheng,Du, Keming,Zheng, Feixiang,Chu, Jinxiang,Sun, Zhongfu,Zhang, Lingxian.

[4]Image segmentation of G bands of Triticum monococcum chromosomes based on the model-based neural network. Cai, N,Hu, KH,Xiong, HT,Li, SY,Su, WF,Zhu, FS.

[5]Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones. Shufeng Zhang,Yuxi Chen,Weizhen Liu,Bang Liu,Xiang Zhou. 2023

[6]Detection method of rice blast based on 4D light field refocusing depth information fusion. Yang N.,Chang K.,Tang J.,Xu L.,He Y.,Huang R.,Yu J.. 2023

[7]A Machine Vision-Based Method for Tea Buds Segmentation and Picking Point Location Used on a Cloud Platform. Lu, Jinzhu,Yang, Zhiming,Sun, Qianqian,Gao, Zongmei,Ma, Wei. 2023

[8]ONLINE DETECTION SYSTEM FOR CRUSHED RATE AND IMPURITY RATE OF MECHANIZED SOYBEAN BASED ON DEEPLABV3+ [基于 DeepLabV3+的大豆机械化收获破碎率和含杂率在线检测系统]. Man Chen,Gong Cheng,Jinshan Xu,Guangyue Zhang,Chengqian Jin. 2023

[9]DeeplabV3+-based navigation line extraction for the sunlight robust combine harvester. Gong Cheng,Chengqian Jin,Man Chen. 2024

[10]ONLINE DETECTION SYSTEM FOR CRUSHED RATE AND IMPURITY RATE OF MECHANIZED SOYBEAN BASED ON DEEPLABV3+. Chen, Man,Cheng, Gong,Xu, Jinshan,Zhang, Guangyue,Jin, Chengqian. 2023

[11]Wheat Full-Width harvesting navigation line extraction method using improved Swin-Transformer. Gong Cheng,Chengqian Jin,Man Chen,Zeyu Cai,Zheng Liu. 2025

[12]Image Segmentation-Based Oilseed Rape Row Detection for Infield Navigation of Agri-Robot. Guoxu Li,Feixiang Le,Shuning Si,Longfei Cui,Xinyu Xue. 2024

[13]Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning. Bo Han,Jingjing Zhang,Rolla Almodfer,Yingchao Wang,Wei Sun,Tao Bai,Luan Dong,Wenjing Hou. 2025

[14]A counting method of whiteflies on crop leave images captured by AR glasses based on segmentation and improved YOLOv11 models. Shilong Zhao,Jun Lyu,Shuhua Liu,Zelin Feng,Heping Ling,Jiabao Jiao,Zhaoxin Ni,Baojun Yang,Qing Yao,Ju Luo. 2025

[15]FEL-YoloV8: A New Algorithm for Accurate Monitoring Soybean Seedling Emergence Rates and Growth Uniformity. Yu, Xun,Jiang, Tiantian,Zhu, Yanqin,Li, Liming,Fan, Fan,Jin, Xiuliang. 2025

[16]MPG-SwinUMamba: High-Precision Segmentation and Automated Measurement of Eye Muscle Area in Live Sheep Based on Deep Learning. Zhou Zhang,Yaojing Yue,Fuzhong Li,Leifeng Guo,Svitlana Pavlova. 2025

[17]Deciphering the Routes of invasion of Drosophila suzukii by Means of ABC Random Forest. Fraimout, Antoine,Debat, Vincent,Fellous, Simon,Hufbauer, Ruth A.,Foucaud, Julien,Loiseau, Anne,Estoup, Arnaud,Hufbauer, Ruth A.,Pudlo, Pierre,Marin, Jean-Michel,Price, Donald K.,Cattel, Julien,Chen, Xiao,Depra, Marindia,Duyck, Pierre Francois,Guedot, Christelle,Kenis, Marc,Kimura, Masahito T.,Loeb, Gregory,Martinez-Sanudo, Isabel,Pascual, Marta,Richmond, Maxi Polihronakis,Shearer, Peter,Singh, Nadia,Tamura, Koichiro,Zhang, Jinping.

[18]Rapid classification of peanut varieties for their processing into peanut butters based on near‐infrared spectroscopy combined with machine learning. Hongwei Yu,Sara W. Erasmus,Qiang Wang,Hongzhi Liu,Saskia M. van Ruth. 2023

[19]Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs. Yang Liu,Haikuan Feng,Jibo Yue,Yiguang Fan,Xiuliang Jin,Xiaoyu Song,Hao Yang,Guijun Yang. 2022

[20]Characterization and discrimination of the flavor profiles of Chinese indigenous sheep breeds via electronic sensory, smart instruments and chemometrics. Can Xiang,Shaobo Li,Dequan Zhang,Caiyan Huang,Yingxin Zhao,Xiaochun Zheng,Zhenyu Wang,Li Chen. 2023

作者其他论文 更多>>