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Currently private and government agencies use remote sensing images (RSI) for various applications from military applications to agriculture growth. The images can be multispectral, panchromatic, ultra-spectral, or hyperspectral of terra bytes. RSI classification is considered one important application for remote sensing. Climate change detection especially affects numerous aspects of day-to-day lives, for instance, forestry management, weather forecasting, transportation, agriculture, road condition monitoring, and the detection of the natural atmosphere. Conversely, certain research works had a focus on classification of actual weather phenomenon images, generally depending on visual observations from humans. The conventional artificial visual difference between weather phenomena will take more time and error-prone. This paper develops a new reinforcement learning based climate change analysis on satellite multispectral image processing (RLCCA-SMSIP) technique. In order to properly determine climate change, the RLCCA-SMSIP technique employs residual network (ResNet-101) model for feature extraction. Next, deep reinforcement learning (DRL) approach is utilized for climate classification. Finally, parameter selection of the RLCCA-SMSIP technique involves sine cosine algorithm (SCA) for DRL model. For assuring the enhanced outcomes of the presented RLCCA-SMSIP model, comprehensive comparison results are assessed. The obtained values denote the supremacy of the RLCCA-SMSIP model on climate classification.
How to Cite
Firdaus, M. Y. ., & Kamil, M. . (2022). Climate Change Analysis Based on Satellite Multispectral Image Processing in Feature Selection Using Reinforcement Learning. International Journal of Communication Networks and Information Security (IJCNIS), 14(2), 261–272. https://doi.org/10.17762/ijcnis.v14i2.5520
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