HYPERSPECTRAL IMAGE CLASSIFICATION TO DETECT WEED INFESTATIONS AND NITROGEN STATUS IN CORN

HYPERSPECTRAL IMAGE CLASSIFICATION TO DETECT WEED INFESTATIONS AND NITROGEN STATUS IN CORN
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用于检测玉米杂草侵染和氮状况的高光谱图像分类

DOI：
10.13031/2013.12943
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发表时间：
2003
期刊：
Transactions of the ASABE
影响因子：
1.5
通讯作者：
A. Viau
A. Viau
中科院分区：
农林科学4区
文献类型：
--
作者：
P. Goel;S. Prasher;J. Landry;R. Patel;A. Viau

文献摘要

被引文献

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高光谱航空图像用于检测杂草侵扰和氮肥水平的潜力 玉米（Zea mays L.）作物进行了评价。使用紧凑型机载光谱成像仪（CASI）获取 在麦吉尔大学麦克唐纳校区的洛德农学研究中心进行的一项实地实验中， 加拿大魁北克大学。玉米在四种杂草管理策略下生长（无杂草控制，控制草， 阔叶杂草的控制和杂草的完全控制）与60、120和250 N的氮肥量的因子组合 公斤/公顷。在抽雄阶段（种植后66天）获得航空图像。对于远程的分类 感测图像、各种广泛使用的监督分类算法（最大似然，最小距离， Mahalanobis距离、平行六面体和二进制编码）和更复杂的分类方法（光谱角 映射器和线性光谱解混）进行了研究。很难区分杂草和 氮素处理同时进行。然而，当只有一个因素时， 杂草或氮素处理。使用不同的分类器，取决于为 分类准确率为65.84%~ 99.46%。没有发现单个分类器对所有条件都有用。
The potential of hyperspectral aerial imagery for the detection of weed infestation and nitrogen fertilization level in a corn (Zea mays L.) crop was evaluated. A Compact Airborne Spectrographic Imager (CASI) was used to acquire hyperspectral data over a field experiment laid out at the Lods Agronomy Research Centre of Macdonald Campus, McGill University, Quebec, Canada. Corn was grown under four weed management strategies (no weed control, control of grasses, control of broadleaf weeds, and full weed control) factorally combined with nitrogen fertilization rates of 60, 120, and 250 N kg/ha. The aerial image was acquired at the tasseling stage, which was 66 days after planting. For the classification of remote sensing imagery, various widely used supervised classification algorithms (maximum likelihood, minimum distance, Mahalanobis distance, parallelepiped, and binary coding) and more sophisticated classification approaches (spectral angle mapper and linear spectral unmixing) were investigated. It was difficult to distinguish the combined effect of both weed and nitrogen treatments simultaneously. However, higher classification accuracies were obtained when only one factor, either weed or nitrogen treatment, was considered. With different classifiers, depending on the factors considered for the classification, accuracies ranged from 65.84% to 99.46%. No single classifier was found useful for all the conditions.