A Learnable Blur Kernel for Remote Sensing Image Retrieval

2020 
With the explosive increase of remote sensing images, content-based remote sensing image retrieval (CBRSIR) has aroused widespread attention. Convolutional Neural Network (CNN) based methods are widely used in CBRSIR due to the development of deep learning. However, common used CNN models have difficulties in holding shift-invariant property due to the widely used down-sampling method, which means a little shift of input may cause a mutation of feature representation. To mitigate the absence of shift-invariant in down-sampling, we propose the learnable blur kernel (LBK), that can enhance the feature extraction capability by leveraging more context information. We build on this concept without extra cost, which can be simply integrated with modern CNNs architecture. Our method is validated on the public remote sensing dataset and compared with other retrieval methods. The overall experimental results show that the proposed method achieves outstanding performance.
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