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Convolutional neural networks with deep supervised feature learning for remote sensing scene classification

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Figure

Figure 1. Block diagram of the CNN-SLF where the three loss function modules are shown
Figure 2. Validation set accuracy as a function of training epoch for the baseline and the proposedCNN-SFL method, considering the values for the feature space representation, namely 32, 64 and 128dimensional spaces.
Table 1well as the temperature parameter, we explore the impact of weighting between the L1 and L2 given by parameters λ1 and λ2, as τ.
Figure 4. 2D mapping of samples and class representatives obtained from (a) the ResNet101 baselinemodel and (b) the CNN-SFL model.
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