不同种植设施背景蔬菜作物无人机高光谱精细分类

胡顺石; 杨斌; 黄英; 岑奕; 戚文超

doi:10.11834/jrs.20222054

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不同种植设施背景蔬菜作物无人机高光谱精细分类
Fine classification of vegetable crops covered with different planting facilities using UAV hyperspectral image
2024年28卷第1期页码：280-292
纸质出版日期： 2024-01-07 ，
DOI： 10.11834/jrs.20222054
稿件说明：

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胡顺石，杨斌，黄英，岑奕，戚文超.2024.不同种植设施背景蔬菜作物无人机高光谱精细分类.遥感学报，28（1）： 280-292

Hu S S， Yang B， Huang Y， Cen Y and Qi W C. 2024. Fine classification of vegetable crops covered with different planting facilities using UAV hyperspectral image. National Remote Sensing Bulletin， 28（1）：280-292
胡顺石，杨斌，黄英，岑奕，戚文超.2024.不同种植设施背景蔬菜作物无人机高光谱精细分类.遥感学报，28（1）： 280-292 DOI： 10.11834/jrs.20222054.

Hu S S， Yang B， Huang Y， Cen Y and Qi W C. 2024. Fine classification of vegetable crops covered with different planting facilities using UAV hyperspectral image. National Remote Sensing Bulletin， 28（1）：280-292 DOI： 10.11834/jrs.20222054.

摘要

中国蔬菜产业规模大、产值高，是促进农民增收和农村农业经济发展的支柱产业。快速准确地获取区域尺度蔬菜种植结构信息对于农业现代化、自动化和精细化等具有重要意义。无人机高光谱遥感技术具有快速机动灵活和“图谱合一”的优势，在作物精细分类中具有广泛应用前景。然而蔬菜作物种植规模差异大、农业景观破碎度高，同时还受地膜、大棚和防鸟网覆盖等影响，无人机高光谱图像易产生严重的混合光谱效应，给蔬菜作物精细分类带来了极大的挑战。针对此问题，本研究以湖南省农科院高桥科研基地蔬菜种植区为例，获取无人机高光谱图像，探索采用支持向量机和深度学习方法对不同蔬菜作物进行精细分类。研究结果表明：基于无人机高光谱遥感数据，可以实现不同覆盖背景下的蔬菜作物精细分类；两大分类方法的平均总体精度分别为78.03%和90.75%，平均Kappa系数分别为0.7359和0.8887，相较于支持向量机方法，基于深度学习的分类方法获得的精细分类效果更加理想，三维卷积神经网络和引入注意力机制的卷积神经网络可以有效提取图像中的光谱—空间特征信息，在蔬菜作物精细分类中体现出更好的分类效果；蔬菜作物在大尺度地块上空间纹理特征明显，而在小地块尺度上差异较大，宜采用不同深度学习方法对其进行精细分类；不同覆盖背景与蔬菜作物产生混合光谱效应，对作物精细分类效果影响显著。

Abstract

With large-scale and high-output values

the vegetable industry of China is a pillar industry to promote the income increase of farmers and the development of rural agricultural economy. Rapidly and accurately obtaining the structural information of vegetable crop planting is of considerable importance for agricultural modernization

automation

and precision. With the advantages of fast mobility

flexibility

and image-spectrum merging

Unarmed Aerial Vehicle (UAV) hyperspectral remote sensing has wide prospects in fine classification of crops. However

vegetable crop planting scales and modes have considerable variations

and the fragmentation of agricultural landscape is high in China. The vegetable crops are also affected by the coverage of plastic film

greenhouse

and bird proof net

which easily produced the mixed spectral effect in UAV hyperspectral images and also introduced considerable challenges to the fine classification of vegetable crops.

Hyperspectral images of Gaoqiao scientific research base of Hunan Academy of Agricultural Sciences were obtained by UAV. The field survey revealed that the area contains 14 ground feature categories

including eggplant

towel gourd

rice

pepper

and tomato. Support Vector Machine (SVM) is widely used in crop classification due to low requirements for data and excellent generalization capability. Meanwhile

deep convolution neural network structures can automatically learn the abstract features of images and obtain high-level and rich semantic information of samples to successfully complete the classification task. Therefore

SVM and Deep Learning (DL) methods were applied to the classification of vegetable crops in this study. Unlike other hyperspectral classification verification experiments that randomly select training sets

training and test samples were manually selected in this study to reduce the spatial correlation between training and test sets

and the performance of different classification methods was evaluated using confusion matrix.

The results showed that based on hyperspectral images obtained by UAVs

the average overall accuracy of vegetable crop classification using SVM and DL methods is 78.03% and 90.75%

respectively

and the average Kappa coefficients are 0.7359 and 0.8887

respectively. Compared with the SVM methods

the fine classification effects obtained by the DL methods are more ideal. This finding is attributed to the effective extraction of spectral and spatial feature information from the image using the three-dimensional convolutional neural network and the convolutional neural network with attention mechanism

thus demonstrating a superior performance in the classification of vegetable crops. The spatial texture characteristics of vegetable crops are observed on large-scale plots

while they are various on small-scale plots. Thus

using different DL methods for the classification of vegetable crops on different scale plots is appropriate.

Vegetable crops under different planting facilities were classified in this study using UAV hyperspectral images. Under the influence of complex backgrounds such as plastic films

bird nets

and greenhouses

satisfactory performance was still achieved using SVM and DL methods

which can provide technological support for the modernization

automation

and refinement of regional vegetable crop management.

关键词

精细分类蔬菜作物无人机高光谱大棚地膜

Keywords

fine classificationvegetable cropsUnmanned Aerial Vehicle (UAV)hyperspectralgreenhousesmulch film

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