## 2 Related Works  
In recent years, the rapid advancement of computer technology has given rise to a multitude of machine learning algorithms, thereby offering new pathways for automated remote sensing applications. Machine learning algorithms capitalize on data and experience to uncover patterns, regularities, and correlations hidden in large datasets, which are then utilized for tasks such as prediction, classification, and optimization. Depending on the learning approach, machine learning can generally be categorized into supervised learning, unsupervised learning, and reinforcement learning. Traditional machine learning methods--such as decision trees, support vector machines (SVM), K-means clustering, and principal component analysis--have been widely applied in image segmentation to extract target objects from imagery [18]. For instance, leveraging remote sensing data from Sentinel-2 and Landsat-8, [PERSON] [19] and colleagues developed classification models based on decision trees and random forest algorithms, using NDVI time series and phenological parameters. Their approach successfully differentiated various crops and their rotation patterns, significantly enhancing the accuracy of agricultural land use maps, cropping systems, and crop types. Similarly, [PERSON] [20] conducted a comparative study of four machine learning algorithms--random forest (RF), SVM, K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost)--combining high-resolution multispectral imagery with LiDAR data to classify tree species across regions at different elevations. Despite the extensive use of traditional machine learning methods, they still exhibit certain limitations when applied to complex, high-dimensional remote sensing data [21]. These limitations include the strong dependence on handcrafted features and insufficient capacity to capture non-linear feature relationships [22]. With the rapid evolution of deep learning--especially the advent and widespread application of convolutional neural networks (CNNs)--remote sensing image classification has undergone a transformative leap. Through multi-layer convolutional structures, CNNs can autonomously extract spatial characteristics and spectral information from data, offering superior adaptability and representational power for large-scale remote sensing tasks. Their outstanding performance in image classification, object detection, and semantic segmentation surpasses that of traditional methods and provides more effective means for the detailed classification of intricate terrain types. Building on these advancements, [PERSON] [23] and collaborators introduced a multi-channel CNN-based object detection method to identify trees suspected of infection by pine wilt disease (PWD). The model employs a two-stage network structure consisting of a region proposal network (RPN) and a Fast R-CNN, where the RPN generates candidate regions and the Fast R-CNN classifies and refines the detection predictions. Through this two-stage optimization, the loss function converges to a minimal value, enhancing the overall detection accuracy. Likewise, [PERSON] [24] utilized a fully convolutional neural network (combining ResNet-18 with a DeepLabv3+ architecture) and morphological operations to classify and extract the crowns of palm trees in the Amazon Basin, achieving high classification accuracy. In another study, [PERSON] [25] proposed a gated recurrent convolutional neural network (G-RecConNN) that couples a CNN for feature extraction with a GRU to capture temporal dependencies in image sequences, thus facilitating crop disease classification and early prediction. Extending this body of work, [PERSON] [26] designed the U2-Net model based on U-Net, incorporating an encoder-decoder structure with residual connections. Its dual nested U-shaped architecture adeptly extracts multi-scale features and, when applied to UAV-based visible-light imagery, attains high-precision olive tree crown contour detection.