基于最优子集选择的水稻穗无人机图像分割方法

Best Subset Selection Based Rice Panicle Segmentation from UAV Image

为探索有效的稻穗识别特征选取方法,解决基于无人机数码影像水稻产量估测中图像颜色空间各个通道或指数对水稻穗识别能力不清的问题,利用2017年和2018年沈阳农业大学超级稻成果转化基地水稻试验田无人机高清数码影像、地面小区样方内水稻穗数量等实测数据,构建了水稻穗、叶、背景的3分类图像样本库,应用最优子集选择(Best subset selection)算法分析了RGB和HSV颜色空间各个通道或指数对水稻穗的识别能力,提取适合东北粳稻稻穗图像分割的7种特征参数,以此特征为输入构建了基于BP神经网络的稻穗分割模型,进一步对稻穗图像进行连通域分析,获取稻穗数量,并与地面实测数据进行比较。结果表明:最优子集选择算法获取的稻穗像素分割特征参数为R、B、H、S、V、GLI、ExG等7种,飞行高度为3 m时,稻穗分割效果最好,对应的交叉验证均方误差MSE为0.0363;构建的稻穗分割模型可有效实现东北粳稻稻穗的提取,3、6、9 m飞行高度下,拍摄图像稻穗数量提取的均方根误差分别为9.03、11.21、13.10,平均绝对百分误差分别为10.60%、14.88%和17.16%。

In order to solve the problem that the ability of panicle recognition by each channel or index of digital image color space is not clear,in rice yield estimation based on UAV image,an effective panicle characteristic-selecting method was developed.The field experimental data were collected from super rice achievement transformation base of Shenyang Agricultural University in 2017 and 2018,including high-resolution digital image collected with UAV and the number of panicles in each sampling square in rice plots.In order to identify the panicle recognition ability of channels or index in the RGB and HSV color space,a tri-classification image sample library of rice panicle,leaf and background was firstly constructed,and features extraction was performed by using the best subset selection(BSS)algorithm.The BSS extracted the seven characteristic parameters which were suitable for panicle segmentation of japonica rice in Northeast China,and used as input to panicle segmentation model based on BP neural network.The recognized panicle pixels from segmentation model were clustered by connected component analysis and the number in each sampling square was estimated,which can be compared with field measurement results for quantitively error analyzing.The results showed that the best subset selection based feature extraction performed best when the number of the feature was 7(features were R,B,H,S,V,GLI and ExG,respectively),and the latitude was 3 m.The corresponding minimum MSE of cross validation is 0.0363.The rice panicle segmentation model can effectively achieve the extraction of japonica rice panicle in Northeast China,with the average RMSE and MAPE of rice panicle number extraction in three flight altitude images taken by 3 m,6 m and 9 m were 9.03 and 10.60%,11.21 and 14.88%,13.10 and 17.16%,respectively.