Huaxiang Song*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1381-1398, 2023, DOI:10.32604/iasc.2023.039315
Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it
appears that training optimization strategies have received less attention in
relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s
effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison.
The overall accuracy (OA) trained by the FST-CNN algorithm on the same
model architecture and dataset is treated as an evaluation baseline. Results
show that there is a maximal OA… More >
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