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RSS FeedsRemote Sensing, Vol. 14, Pages 2483: Pair-Wise Similarity Knowledge Distillation for RSI Scene Classification (Remote Sensing)

 
 

22 may 2022 10:09:18

 
Remote Sensing, Vol. 14, Pages 2483: Pair-Wise Similarity Knowledge Distillation for RSI Scene Classification (Remote Sensing)
 


Remote sensing image (RSI) scene classification aims to identify the semantic categories of remote sensing images based on their contents. Owing to the strong learning capability of deep convolutional neural networks (CNNs), RSI scene classification methods based on CNNs have drawn much attention and achieved remarkable performance. However, such outstanding deep neural networks are usually computationally expensive and time-consuming, making them impossible to apply on resource-constrained edge devices, such as the embedded systems used on drones. To tackle this problem, we introduce a novel pair-wise similarity knowledge distillation method, which could reduce the model complexity while maintaining satisfactory accuracy, to obtain a compact and efficient deep neural network for RSI scene classification. Different from the existing knowledge distillation methods, we design a novel distillation loss to transfer the valuable discriminative information, which could reduce the within-class variations and restrain the between-class similarity, from the cumbersome model to the compact model. This method could obtain the compact student model with higher performance compared with existing knowledge distillation methods in RSI scene classification. To be specific, we distill the probability outputs between sample pairs with the same label and match the probability outputs between the teacher and student models. Experiments on three public benchmark datasets for RSI scene classification, i.e., AID, UCMerced, and NWPU-RESISC datasets, verify that the proposed method could effectively distill the knowledge and result in a higher performance.


 
120 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 2480: Development and Evaluation of a Real-Time Hourly One-Kilometre Gridded Multisource Fusion Air Temperature Dataset in China Based on Remote Sensing DEM (Remote Sensing)
Remote Sensing, Vol. 14, Pages 2485: The Relative Roles of Climate Variation and Human Activities in Vegetation Dynamics in Coastal China from 2000 to 2019 (Remote Sensing)
 
 
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