一种面向农业无人机影像分割的尺度参数自动确定方法

doi:10.3864/j.issn.0578-1752.2020.17.007

摘要/Abstract

摘要：

【目的】自动提取影像中作物种植区域信息,对于推动无人机高分辨率影像在精准农业中的应用具有重要意义。本研究针对分割评价函数中加权局部方差法（weighted local variance,WLV）的缺陷,面向农业无人机影像高精度自动分割的需求,提出改进方法并基于不同作物田间试验数据进行对比验证。【方法】针对WLV没有充分考虑分割对象内部同质性的问题,本研究在WLV的基础上增加对象间同质性的计算,提出了改进加权局部方差法（improved weighted local variance,IWLV）。设计玉米氮肥试验和小麦水肥试验,获取不同作物不同时期及长势下的无人机影像。基于获取的无人机影像,设置不同情景,分别耦合主流分割算法与WLV、IWLV法开展影像分割,将它们的分割结果与人机交互分割结果进行对比,并基于单尺度对象精度（single-scale object accuracy,SOA）法进行评价。【结果】基于WLV法选择的最优分割尺度往往偏大,分割影像时会存在欠分割现象,而基于IWLV法选择的分割尺度进行分割的结果与人机交互分割结果更为接近。对于所有设定的分割情景,IWLV法获得了更高的SOA值。【结论】与WLV法相比,本研究提出的IWLV法可以更准确实现无人机影像分割中尺度参数的自动确定。

关键词: 尺度参数, 图像分割, 无人机影像, 改进加权局部方差法

Abstract:

【Objective】The automatic extraction of crop planting area in the image is of great significance for promoting the application of high-resolution drone images in precision agriculture. At present, the method based on designing segmentation evaluation function is most commonly used in the study of automatically determining segmentation scale parameters. In order to meet the needs of high-precision automatic segmentation of agricultural drone images, an improved evaluation function was proposed to solve the defects of the Weighted Local Variance (WLV) method in this study, and the proposed method was verified based on field experiments of different crops.【Method】With considering that WLV method does not consider the internal homogeneity of segmented objects, this study designed the Improved Weighted Local Variance (IWLV) method with adding the calculation of inter-object homogeneity on the basis of WLV formula. The nitrogen experiments of corn and water and nitrogen coupling experiment of wheat were designed. During corn and wheat growing season, drone images were obtained in different growth stages with crop in different vigor. Based on the obtained UAV images, different scenarios were set. The mainstream segmentation algorithm was combined with WLV method and IWLV method to perform image segmentation, respectively. Their segmentation results were compared with human-machine interactive segmentation results, and evaluated based on Single-scale Object Accuracy (SOA).【Result】The selected scale parameter by the WLV method tended too large, which resulted in under-segmentation during segmenting images. While, based on the selected scale parameter by IWLV method, the segmentation result was correspond well with human-machine interactive segmentation results. The IWLV method had higher SOA values for all designed scenarios.【Conclusion】Compared with the WLV method, the proposed IWLV method in this study had higher accuracy when determining the segmentation scale parameter.

Key words: scale parameter, image segmentation, drone image, improved weighted local variance method

石雅娇,陈鹏飞. 一种面向农业无人机影像分割的尺度参数自动确定方法[J]. 中国农业科学, 2020, 53(17): 3496-3508.

SHI YaJiao,CHEN PengFei. A Method for the Automatic Determination of Scale Parameter During Segmenting Agricultural Drone Images[J]. Scientia Agricultura Sinica, 2020, 53(17): 3496-3508.

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方法 Method	N₁S₁	N₂S₁	N₃S₁	N₄S₁	N₅S₁	N₁S₂	N₂S₂	N₃S₂	N₄S₂	N₅S₂
WLV	0.626	0.693	0.675	0.654	0.722	0.747	0.755	0.682	0.726	0.711
IWLV	0.770	0.765	0.767	0.770	0.795	0.768	0.811	0.796	0.807	0.771

方法 Method	N₁W₁	N₂W₁	N₃W₁	N₄W₁	N₅W₁	N₁W₂	N₂W₂	N₃W₂	N₄W₂	N₅W₂
WLV	0.675	0.535	0.548	0.553	0.661	0.618	0.430	0.446	0.536	0.646
IWLV	0.675	0.660	0.717	0.602	0.705	0.667	0.716	0.708	0.685	0.648