Geospatial object detection in remote sensing images is a challenging subject since objects in remote sensing images are dense, multioriented, and multiscale. We present an attention network for object detection in remote sensing images. Through channel attention and spatial attention, the framework pays more attention to important channels and emphasizes position information of objects. Meanwhile, saliency learning is proposed to enhance objects information. Furthermore, saliency loss is added to the loss function to guide network learning in the training stage. In addition, multiscale feature module is added into the network to capture scale variations. Experimental results on public remote sensing image datasets validate the effectiveness of the proposed method.