关键词: 猕猴桃园, 面向对象, 时序特征, 小波纹理, 随机森林

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.

Key words: Kiwifruit orchard, Object‐oriented, Temporal features, Wavelet texture, Random forest