Low inter-class variance and complex spatial details exist in ground objects of the coastal zone, which leads to a challenging task for coastal land cover classification (CLCC) from high-resolution remote sensing images. Recently, fully convolutional neural networks have been widely used in CLCC. However, the inherent structure of the convolutional operator limits the receptive field, resulting in capturing the local context. Additionally, complex decoders bring additional information redundancy and computational burden. Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. Among them, lightweight global feature attention modules are employed to aggregate multi-scale spatial details in the decoding stage. Meanwhile, position and channel attention modules with long-range dependencies are embedded to enhance feature representations of specific categories by capturing the multi-dimensional global context. Additionally, multiple objective functions are introduced to supervise and optimize feature information at specific scales. We apply the proposed method in CLCC tasks of two study areas and compare it with other state-of-the-art approaches. Experimental results indicate that the proposed method achieves the optimal performances in encoding long-range context and recognizing spatial details and obtains the optimum representations in evaluation indexes.