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The utilization of remote sensing (RS) technology has resulted in the extensive
accessibility of a significant amount of satellite image data. In order to ensure the
successful execution of the RS in real-life scenarios, it is imperative to create effective and
adaptable solutions that can be utilized across different transdisciplinary domains. Deep
Convolutional Neural Networks (CNNs) are frequently used to accomplish the goal of fast
analysis and precise categorization in RS imaging. This study introduces a unique residual
network known as ResNet101. The network comprises FC-1024 fully connected layers,
dropout layers, a thick layer, and data augmentation algorithms. To resolve the issue of
similarity between different classes, architectural enhancements are implemented. On the
other hand, imbalanced classes are dealt with by employing data augmentation techniques.
The ResNet101 model use the rigorous Large-Scale Cloud pictures Dataset for
Meteorology Research (LSCIDMR), which has 10 classes and a multitude of highresolution photos. The goal of the model is to precisely classify these photos into their
respective categories. The model we have created outperforms numerous previously
published deep learning algorithms in terms of Precision, Accuracy, and F1 scores. The
accuracy reaches up to 99% and approximately 92%, respectively.