| Citation: | CHEN C，ZHAO W. Remote sensing target detection based on dynanic feature selection[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：702-709 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0300
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In the field of remote sensing image target detection, there still are challenges in oriented object detection. Convolutional neural network is subject to a fixed spatial structure when extracting information, and sampling locations cannot focus on objects. The scale of the remote sensing image varies greatly, and different objects require receptive fields of different scales to obtain feature map. Meanwhile, feature map with a single-scale receptive field cannot contain all effective information. In response to the first problem, deformable alignment convolution was proposed, which can first adjust the sampling locations according to the region of interest, and further learn slight offsets according to feature map, so that sampling locations can focus on objects and realize dynamic feature selection. For the second question, receptive field adaptive module based on deformable alignment convolution was proposed to fuse feature map with receptive fields of different scales and adaptively adjust the receptive field of neurons. Extensive experiments on public datasets showed that this method can improve the accuracy of remote sensing image target detection.
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