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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification
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AI
DOI：10.26599/BDMA.2021.9020017.png)
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Abstract
En 中文
As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.
Keywords:
Training
Satellites
Predictive models
Feature extraction
Cyclones
Real-time systems
Convolutional neural networks
satellite images
satellite image classification
cyclone prediction
Deep Convolutional Neural Network (DCNN)
features
layers
down-sampling process
Journal
IF:
6.2
Papers:
274
Citations:
1.0K
