Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops
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摘要: 农作物种植结构包含农作物种类、数量结构和空间分布特征等信息,是农业科学管理的基础。在不考虑农作物时间序列最佳窗口期的前提下,以石津灌区为研究区,基于高分一号(GF-1)WFV影像计算并分析纹理特征在农作物分类识别中的能力。并在纹理特征分类效果相对较差的时相内引入植被指数,从而弥补纹理在农作物表达上的缺陷。经过对比各组分类结果,可以发现: 在作物结构明显的4,8月份,单独纹理特征的分类精度可以达到80%以上,但是在5,6,7,9月等农作物最复杂的时间段内,分类精度仍低于80%。将植被指数与纹理特征组合后,这4个月份的分类结果有了很大改善,总体分类精度均大于80%,基本满足农业动态监测的需求; 与单独纹理相比,精度提高2.27%~9.75%, Kappa系数提高0.02~0.16; 利用夏玉米的验证样本进行验证,识别精度可以达到98%,识别效果相对完整,破碎程度达到最小化,与其他类别区分度也达到了最优。同时也证明了GF-1WFV纹理特征在农作物种植结构提取中的可用性,尤其是在作物结构相对明显的月份内,可以为影像的农作物提取提供一些有效的信息。Abstract: The crop planting structure consists of information such as crop species, quantity structure, and spatial distribution characteristics, and it serves as the basis for agricultural scientific management. Taking the Shijin irrigation area, Hebei Province as the study area and on the premise of not considering the optimal window period of crop time series, this study calculates and analyzes the ability of texture features in crop classification and identification based on GF-1WFV images. Meanwhile, the vegetation index is introduced into the time phase in which the classification effects based on texture features are poor, in order to make up for the defects of texture in the expression of crops. According to the comparison of the classification results of various groups, the classification accuracy of individual texture features reached greater than 80% in April and August when the crop structure is obvious but was still less than 80% in May, June, July, and September when crops are the most complex. After combining the texture features with the vegetation index, the classification results of the crops in these four months were greatly improved. In detail, the overall classification accuracy was greater than 80%, which basically meets the need for agricultural dynamic monitoring. Meanwhile, the accuracy was improved by 2.27%~9.75 % and the Kappa coefficient was increased by 0.02~0.16 compared to the individual texture features. As verified using summer maize samples, the recognition accuracy reached up to 98%, the recognition effects were relatively complete, the fragmentation degree was the minimum, and the optimal discrimination from other crop categories was achieved. Meanwhile, it also proved that the texture features based on GF-1WFV images can be applied to the extraction of the crop planting structure, especially in the months when the crop structure is relatively obvious, and they can provide some effective information for the information extraction of crops base on images.
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Key words:
- GF-1 WFV /
- crop planting structure /
- classification /
- texture /
- accuracy /
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