Record Details

Segmentation of satellite images using machine learning algorithms for cloud classification

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Segmentation of satellite images using machine learning algorithms for cloud classification
 
Creator Sebastian, Sruthy
Kumar, Lakshmi Sutha
Annadurai, Pugazhenthi
 
Subject Fuzzy-C-Means
INSAT-3DR
Random forest
K-Means clustering
 
Description 12-18
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.
 
Date 2021-09-09T10:39:07Z
2021-09-09T10:39:07Z
2021-03
 
Type Article
 
Identifier 0975-105X (Online); 0367-8393 (Print)
http://nopr.niscair.res.in/handle/123456789/58084
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJRSP Vol.50(1) [March 2021]