Extraction of Kiwifruit Orchards in Southern Mountainous Regions Based on Object-Oriented Classification Methods

Zhao Yining; Tian Zunhua; Wang Yingying; Duan Liangxia; Xie Hongxia; Zhou Qing

doi:10.15933/j.cnki.1004-3268.2026.08.014

Abstract：To accurately map kiwifruit cultivation zones,Songbai Town,Yongshun County,Hunan Province was selected as the experimental area.Based on the domestically developed GF-6 sensor,an object-oriented random forest classification model integrating multi-temporal and textural features was constructed.The classification performance was systematically evaluated across 12 feature combinations:single-temporal(July),single-temporal+textural,and multi-temporal(May,July,September)+textural.The results showed that the multi-temporal+texture feature combination classification model performed better in the extraction of kiwifruit orchards,which could achieve high-precision identification of kiwifruit orchards in the study area.The multi-temporal feature was the key factor to improve the classification accuracy.Compared with the single-temporal+texture feature combination(RF2—RF6),after introducing multi-temporal features(RF7—RF12),the F value increased from 89.35%—91.09%to 91.59%—93.48%,the overall accuracy(OA)increased from 83.65%—87.33%to 91.00%—93.18%,and the Kappa coefficient increased from 0.78-0.80 to 0.86-0.90.Compared with the single-phase feature(RF1),the single-phase+texture feature combination showed limited improvement in classification accuracy.The F value was only increased by 1.03 percentage points,and the Kappa coefficient was only 0.80.In the combination of different texture+multi-temporal features,the wavelet texture(RF9)extracted based on energy statistics performed best.The F value and OA of RF9 reached 93.48%and 93.18%,respectively,which were 1.89 and 2.04 percentage points higher than those of GLCM texture(RF8),and 1.59 and 2.18 percentage points higher than those of multi-temporal features(RF7).These indicate that the object-oriented random forest classification method utilizing multi-temporal+wavelet texture feature combination is an effective approach for high-precision extraction of kiwifruit orchards in complex mountainous terrain.