The tea plantations of Yunnan province are mainly fragmentally distributed in mountainous areas and often mixed with other ground objects, making it difficult to extract tea plantations with high precision based on remote sensing. Combining textures and spatial features based on Object-Oriented method is rarely applied to extract tea plantations in previous crop classification research using multi-spectral imagery. Therefore, it is necessary to explore further the recognition ability for tea plantations by using high spatial resolution and multi-spectral images under the fragmental and mountainous region. In this research, a typical mountainous area located between the northern Xishuangbanna autonomous prefecture and the southern of Pu’er city was used as our study area. A scene image with a 2 m resolution pan-chromatic and 8 m resolution multi-spectral derived from the GF-1 PMS sensor was used as the source data for our research. The eCognition Developer9.0 software was employed to segment the image by multi-resolution segmentation, and the ED3_Modified method was used to evaluate the optimal segmentation scale. Firstly, we constructed 23 dimensions of original features including 14 spectral features, 6 textures and 3 spatial features. Secondly, 16 optimal features were selected to classify by calculating the separation distance of five land-cover types. Thirdly, based on 16 optimal features space, three object-oriented supervised classification methods (Bayes, Decision Tree 5.0 (DT) and Random Forest (RF) were applied to extract the tea plantations of the study area. Finally, filed survey samples and random samples were used to validate the accuracy of tea plantations extraction results, and we compared the classification accuracies of different classification methods. The results showed that for the multi-classification (including tea plantations, forest, cropland, impervious and water body) the overall accuracy (OA)/ Kappa coefficient (Kappa) are Bayes (87.73%/0.70), DT5.0 (91.23%/0.78) and RF (88.52%/0.72) respectively, but for tea plantations, the producer accuracy (PA)/user accuracy (UA) are Bayes (67.23%/75.33%), DT (68.84%/83.83%) and RF (70.54%/87.13%). Compared with the object-oriented RF multi-classification, the OA and Kappa of the object-oriented RF binary classification (tea plantations and others) increased by 3.24% and 0.07, the PA/UA of tea plantations increased by 5.99%/5.61%. Similarly, compared with the pixel-based multi-classification, the OA and Kappa of the object-oriented RF binary classification increased by 23.32% and 0.27, the PA/UA of tea plantations increased by 21.10%/29.03%, respectively. The results indicated that the object-oriented supervised classification methods have the potential for tea plantations extraction, especially the object-oriented RF classification got a higher accuracy. Moreover, the binary classification method has higher accuracy than that of multi-classification for tea plantation extraction. Our object-oriented method that combined textures and spatial features with spectral features is effective for tea plantations extraction, especially when applied to the complex and fragmental mountainous landscape. Our method can meet the application requirements in fine tea plantations identification based on high-spatial resolution and multi-spectral imagery too.