Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Kalyan Kumar Jena, Sourav Kumar Bhoi, Soumya Ranjan Nayak, Ranjit Panigrahi, Akash Kumar Bhoi

Big Data Mining and Analytics · 24 November 2022

In one sentence. A deep convolutional network classifies satellite cloud images from MOSDAC over India with 94% accuracy, aimed at tropical cyclone prediction.

Abstract

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.

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