Machine Learning for Cloud Cover Detection Using Multispectral Satellite Images

Verma, Preeti; Patil, Sunil

doi:10.1007/s40745-021-00367-4

Cited by 2 publications

(1 citation statement)

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“…In [11], ML related technique to estimate solar radiation on earth external was offered. Toward strategy, the ML technique, multispectral (infrared and visible) satellite imageries of actual high-resolution after manifold positions stay as main data.…”

Section: Literature Reviewmentioning

confidence: 99%

Climate Change Analysis Based on Satellite Multispectral Image Processing in Feature Selection Using Reinforcement Learning

Firdaus

Kamil

2022

Int. j. commun. netw. inf. secur.

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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.

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Section: Literature Reviewmentioning

confidence: 99%