Advanced machine learning techniques for satellite image processing

Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approache...

Full description

Saved in:
Bibliographic Details
Main Authors: Kumaraswamy, Eelandula, Kommabatla, Mahender, Reddy, I. Rajasri, Karre, Ravikiran, Kasanagottu, Srinivas, Ramu, Moola
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Classification
Data processing
Image analysis
Image processing
Image segmentation
Land cover
Landslides
Machine learning
Natural disasters
Plants (botany)
Remote sensing
Satellite imagery
Satellite tracking
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0195776