[논문]무인기 기반 영상과 SVM 모델을 이용한 가을수확 작물 분류 - 충북 괴산군 이담리 지역을 중심으로 -

정찬희; 고승환; 박종화

doi:10.7851/ksrp.2022.28.1.057

무인기 기반 영상과 SVM 모델을 이용한 가을수확 작물 분류 - 충북 괴산군 이담리 지역을 중심으로 -
Classification of Fall Crops Using Unmanned Aerial Vehicle Based Image and Support Vector Machine Model - Focusing on Idam-ri, Goesan-gun, Chungcheongbuk-do - 원문보기

농촌계획 : 韓國農村計劃學會誌, v.28 no.1, 2022년, pp.57 - 69

정찬희 (충북대학교 농업생명환경대학 지역건설공학과) , 고승환 (충북대학교 농업생명환경대학 지역건설공학과) , 박종화 (충북대학교 농업생명환경대학 지역건설공학과)

Abstract ▼ AI-Helper

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

주제어

표/그림 (15)

그림 Figure 1. The location of the study area
그림 Figure 2. Input crop cultivation status data using ArcGIS Pro and Farm-Map
그림 Figure 3. Farm-Map for constructing agricultural land status
표 Table 1. Error Matrix for accuracy evaluation
그림 Figure 4. Orthoimage produced using RGB and Multispectral image
표 Table 2. Classification quality according to Kappa coefficient
그림 Figure 5. Optimal variable combination by recursive feature elimination (RFE)
표 Table 3. Number of plots and cultivation area of crops
표 Table 4. Variable selection by recursive feature elimination (RFE)
표 Table 5. Optimal hyperparameters and accuracy of training model
그림 Figure 6. Crop classification results for 4 models and reference data map
표 Table 6. Error Matrix for Model 1: Reflectance-based crop classification model accuracy evaluation
표 Table 7. Error Matrix for Model 2: GLCM-based texture features crop classification model accuracy evaluation
표 Table 8. Error Matrix for Model 3: Reflectance and GLCM based crop classification model accuracy evaluation
표 Table 9. Error Matrix for Model 4: Recursive Feature Elimination (RFE) based crop classification model accuracy evaluation

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