In complex military confrontation environments, where unmanned aerial vehicles (UAVs) conduct automatic reconnaissance and identification of multi-class ground targets using multi-scene remote sensing images, targets often exhibit characteristics such as color similarity with the background, blurred boundaries, and variable background environments. Traditional convolutional networks struggle with low accuracy and robustness in multi-class segmentation of multi-scene remote sensing images under these conditions. To address this issue, we propose a novel multi-scene remote sensing image segmentation algorithm. This algorithm integrates the Residual Cross-layer Multi-scale Channel and Spatial Attention Module (Rs-CMACM) and the Path Aggregation Network (FAN) to enhance key features, suppress irrelevant information, and improve the delineation of target boundaries. Additionally, dynamic data augmentation and an Image Restoration Sub-network (IRSN) are introduced to enhance segmentation accuracy in complex backgrounds. Firstly, Rs-CMACM and FAN are cross-layer integrated into the backbone network of a typical object segmentation network, enhancing the model"s feature extraction capabilities and fusing multi-scale features at different depths, thereby reducing segmentation bias caused by noisy backgrounds. Secondly, the incorporation of dynamic data augmentation and IRSN compels the model to focus on the intrinsic characteristics of images, enabling the extraction of more robust feature representations under various environmental conditions. These improvements significantly enhance the accuracy and robustness of the model in multi-class segmentation tasks for multi-scene remote sensing images, thereby increasing the precision of target segmentation.