基于特征過濾法和Stacking集成學習的無人機影像作物精細分類

摘" 要： 針對目前多種典型作物分類中特征冗余導致同科作物混淆、分類精度低的問題，文中提出一種結合特征過濾法篩選特征和Stacking集成學習的作物精細分類方法。首先，結合敏感波段構造新型植被指數并進行閾值分割，實現作物區域提取；然后，提取不同作物的顏色和紋理特征，進而計算單類作物特征系數和作物間特征差異系數，實現各典型作物的分類特征過濾法優選；最后，構建融合多種機器學習算法的Stacking集成學習作物分類模型，其中第一層的基學習器選擇隨機森林、支持向量機、K?最近鄰算法，第二層的元學習器選擇邏輯回歸模型，實現多種典型作物精細分類。實驗結果表明，所提方法對7種典型作物的總體分類精度和Kappa系數分別為85.2%和83.34%，相比于未進行特征選擇的分類結果分別提升了2.18%和3.68%，具有較高的分類精度，為多種典型作物的精細分類提供了新方法。

關鍵詞： 作物分類； 特征選擇； Stacking集成學習； 植被指數； 閾值分割； 衍生特征

中圖分類號： TN911.73?34； TP751" " " " " " " " " 文獻標識碼： A" " " " " " " " " "文章編號： 1004?373X（2025）07?0001?10

UAV image fine crop classification based on feature filtering method

and Stacking ensemble learning

LIU Zhaohui， YANG Fengbao， ZHANG Lin

（School of Information and Communication Engineering， North University of China， Taiyuan 030051， China）

Abstract： The feature redundancy in multiple typical crop classifications at present leads to confusion and low classification accuracy of crops of the same family， so this paper proposes a crop fine classification method that combines the feature filtering method for feature screen and Stacking ensemble learning. A new type of vegetation index is constructed by combining sensitive bands， and the threshold value is segmented， so as to realize crop region extraction. The color and texture features of different crops are extracted， and then the feature coefficients of a single type of crop and the coefficients of feature differences among crops are calculated， so as to realize the classification feature filtering method preference for each typical crop. Finally， a Stacking ensemble learning crop classification model that integrates multiple machine learning algorithms is constructed. Among them， the random forest （RF）， support vector machine （SVM） and K?nearest neighbor （K?NN） algorithms are selected for the base learner in the first layer， and the logistic regression model is selected for the meta?learner in the second layer， so that the various typical crops are classified finely. The experimental results show that the overall classification accuracy and Kappa coefficient of the proposed method for the seven typical crops are 85.2% and 83.34%， respectively， which are 2.18% and 3.68% higher than the classification results without feature selection. To sum up， the proposed method has high classification accuracy， and can be used as a new method for the fine classification of multiple typical crops.

Keywords： crop classification; feature selection; Stacking ensemble learning; vegetation index; threshold segmentation; derivative feature

0" 引" 言

精準農業是實現農業低耗、高效、環保和優質發展的根本途徑，是提高世界和我國糧食產量的最佳選擇[1?3]。精準農業管理的實現有賴于對多種作物的精細分類，通過對多種典型作物進行精細分類，可以更好地了解作物的空間分布情況和生長情況，為實現作物的準確估產、精確施肥、定點噴藥提供有力的數據支撐[4]。傳統人工采集作物種類的方法費時、耗力、破壞性大且空間覆蓋不全，影響了作物分類的快速化發展[5]。……