  The growing interest in satellite imagery has triggered the need for
efficient mechanisms to extract valuable information from these vast data
sources, providing deeper insights. Even though deep learning has shown
significant progress in satellite image classification. Nevertheless, in the
literature, only a few results can be found on weight initialization
techniques. These techniques traditionally involve initializing the networks'
weights before training on extensive datasets, distinct from fine-tuning the
weights of pre-trained networks. In this study, a novel weight initialization
method is proposed in the context of satellite image classification. The
proposed weight initialization method is mathematically detailed during the
forward and backward passes of the convolutional neural network (CNN) model.
Extensive experiments are carried out using six real-world datasets.
Comparative analyses with existing weight initialization techniques made on
various well-known CNN models reveal that the proposed weight initialization
technique outperforms the previous competitive techniques in classification
accuracy. The complete code of the proposed technique, along with the obtained
results, is available at https://github.com/WadiiBoulila/Weight-Initialization
