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.

中文翻译：

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遥感图像中基于显着性的基于注意力的目标检测

遥感图像中的地理空间物体检测是一个具有挑战性的课题，因为遥感图像中的物体是密集的，多方位的和多尺度的。我们提出了一种用于遥感图像中目标检测的注意力网络。通过渠道关注和空间关注，该框架更加关注重要渠道并强调对象的位置信息。同时，提出了显着性学习以增强对象信息。此外，显着性损失被添加到损失功能，以在训练阶段指导网络学习。另外，多尺度特征模块被添加到网络中以捕获尺度变化。在公共遥感图像数据集上的实验结果验证了该方法的有效性。