Please use this identifier to cite or link to this item:
Title: Segmentation of satellite images using machine learning algorithms for cloud classification
Authors: Sebastian, Sruthy
Kumar, Lakshmi Sutha
Annadurai, Pugazhenthi
Keywords: Fuzzy-C-Means;INSAT-3DR;Random forest;K-Means clustering
Issue Date: Mar-2021
Publisher: NIScPR-CSIR, India
Abstract: Clouds play a significant role in determining the state of a changing weather. Clouds offer useful information for forecasting precipitation and provide measurement for showcasing solar irradiance variability. The influence of specific types of clouds on rainfall prediction and solar radiance has been discussed in this paper. Various segmentation algorithms, clustering algorithms and supervised machine learning algorithms such as K Nearest Neighbors and Random forest have been used to segment/classify the clouds using the dataset obtained from INSAT-3DR satellite. Clouds have been classified into high level clouds (Cirrus clouds), medium level clouds (Alto clouds) and low level clouds (Stratus clouds) in accordance with the altitude and cloud densities. The performance metrics has been found for the segmented images. Parameters that provide optimum results for supervised machine learning algorithms have been explored. On the images, different machine learning algorithms have been compared.
Page(s): 12-18
ISSN: 0975-105X (Online); 0367-8393 (Print)
Appears in Collections:IJRSP Vol.50(1) [March 2021]

Files in This Item:
File Description SizeFormat 
IJRSP Vol.50(1) 12-18.pdf2.23 MBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.