Multiscale object detection in high-resolution remote sensing images via rotation invariant deep features driven by channel attention

Due to the complex background and spatial distribution, it brings great challenge to object detection in high-resolution remote sensing images. In view of the characteristics of various scales, arbitrary orientations, shape variations, and dense arrangement, a multiscale object detection method in high-resolution remote sensing images is proposed by using rotation invariance deep features driven by channel attention. First, a channel attention module is added to our feature fusion and scaling-based single shot detector (FS-SSD) to strengthen the long-term semantic dependence between objects for improving the discriminative ability of the deep features. Then, an oriented response convolution is followed to generate feature maps with orientation channels to produce rotation invariant deep features. Finally, multiscale objects are predicted in a high-resolution remote sensing image by fusing various scale feature maps with multiscale feature module in FS-SSD. Five experiments are conducted on NWPU VHR-10 dataset and achieve better detection performance compared with the state-of-the-art methods.