Abstract
This work presents a multidimensional attention learning-based lightweight convolutional neural network for very-high-resolution (VHR) remote sensing imagery recognition, which incorporates channel attention, spatial attention, and saliency sampler attention into the backbone to improve its recognition performance. Specifically, channel attention can alleviate the interference of original feature cube by adaptively giving different coefficient weights to different feature channels. Spatial attention can emphasize the discriminative regions by using the sum of activation value in different locations of the image to weight the original feature cube. Saliency sampler attention can increase the influence of interesting regions on the final representation according to the saliency priors. In general, different from the existing methods, this work utilizes the constraints rather than the model scale to improve the recognition performances of the network. In addition, the novel flooding loss is used to optimize the network, which can improve the performance of the framework by alleviating the severe overfitting problem.
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