Second, the residual network is conducive to the training of the network, so that the potential performance of the network is released. The overall architecture of the network is shown in Table 1. The MA module is used to replace the 3 × 3 convolutional layer in the residual network ...
However, thanks to the advantages brought by frequency domain angle analysis, the residual performance obtained after removing the two components is still better than most baseline model indicators, and only MAE and CORR indicators are worse than those associated with LSTM. 5. Conclusions and Future...
Although Transformer has many advantages in building extraction, its complexity is still large, and its performance may not be good when the training dataset is small. According to the above literature, the spectral differences, background complexity, and large scale differences of buildings pose a ...
When novel neurons are added to the hidden layers, the learning method tries and aims to achieve maximum correlation between the output of the added neuron and the network’s residual error, which we are seeking to low and decrease. The output layer is directly connected to an input and ...
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The BIT model first uses the CNN backbone network for feature extraction and then sends it to the transformer structure for processing. The multi-head attention in the transformer block can better identify the edge details of buildings, which is improved compared to the IFNet model. The DSAMNet...
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