AN IMPROVISED CONTENT BASED IMAGE RETRIEVAL SYSTEM FOR REMOTE SENSING IMAGES

Image classification is the correctly identifying the objects of an image. Remote sensing image classification is the efficient execution of image categorization of high resolution spatial images for large remote sensing archives. High performance of image categorization known as classification is directly based upon efficient image feature extraction. As information technology and digital devices have advanced billions of individuals are now utilizing the Internet. The volume of digital data in the form of pictures, videos and text grows tremendously every day. One of the biggest concerns is keeping up with the huge online information repositories. Our daily applications are significantly dependent on multimedia content archives on the internet. The digital format is used to store images in web repositories. It can be challenging to find relevant images in the vast multimedia archive. The Remote Sensing Image Retrieval System (RSIR) searches digital images from various remote sensing archives. The user obtains the most relevant and related images from the database based on the attributes of the query image. RSIR has advanced with the development of novel techniques including transfer learning employing pre-trained models and deep learning breakthroughs. Two processes are involved in the Content-Based Image Retrieval System: feature extraction and feature matching. The goal of feature extraction is to reduce the amount of information that describes the total image content using an appropriate distance measure. Feature matching compares the features that were extracted from the database with those that were extracted from the query image. Every database-indexed image is ranked based on how far away it is from the query image. This research goes beyond remote sensing alone. Content Based Image Retrieval System Content Based Image Retrieval (CBIR) could be very useful in forestry, agriculture and geosciences where satellites can acquire images to determine the earth’s geological characteristics at a certain location and time. The fields of oceanography, geology, archaeology and astrology are other application areas for the content based image retrieval system.

Authors

Publication Details

Journal
International Journal of Apllied Mathematics
Published
2025-09-27
DOI
https://doi.org/10.12732/ijam.v38i4s.236
Primary Topic
Image Retrieval and Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AN IMPROVISED CONTENT BASED IMAGE RETRIEVAL SYSTEM FOR REMOTE SENSING IMAGES

Nisha Gupta
International Journal of Apllied Mathematics
Image Retrieval and Classification Techniques
article

AN IMPROVISED CONTENT BASED IMAGE RETRIEVAL SYSTEM FOR REMOTE SENSING IMAGES

Nisha Gupta
article en

Abstract

Image classification is the correctly identifying the objects of an image. Remote sensing image classification is the efficient execution of image categorization of high resolution spatial images for large remote sensing archives. High performance of image categorization known as classification is directly based upon efficient image feature extraction. As information technology and digital devices have advanced billions of individuals are now utilizing the Internet. The volume of digital data in the form of pictures, videos and text grows tremendously every day. One of the biggest concerns is keeping up with the huge online information repositories. Our daily applications are significantly dependent on multimedia content archives on the internet. The digital format is used to store images in web repositories. It can be challenging to find relevant images in the vast multimedia archive. The Remote Sensing Image Retrieval System (RSIR) searches digital images from various remote sensing archives. The user obtains the most relevant and related images from the database based on the attributes of the query image. RSIR has advanced with the development of novel techniques including transfer learning employing pre-trained models and deep learning breakthroughs. Two processes are involved in the Content-Based Image Retrieval System: feature extraction and feature matching. The goal of feature extraction is to reduce the amount of information that describes the total image content using an appropriate distance measure. Feature matching compares the features that were extracted from the database with those that were extracted from the query image. Every database-indexed image is ranked based on how far away it is from the query image. This research goes beyond remote sensing alone. Content Based Image Retrieval System Content Based Image Retrieval (CBIR) could be very useful in forestry, agriculture and geosciences where satellites can acquire images to determine the earth’s geological characteristics at a certain location and time. The fields of oceanography, geology, archaeology and astrology are other application areas for the content based image retrieval system.

International Journal of Apllied MathematicsVol. 38(4s)
Openalex Percentile: Top 74%
Image Retrieval and Classification Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

AN IMPROVISED CONTENT BASED IMAGE RETRIEVAL SYSTEM FOR REMOTE SENSING IMAGES — Nisha Gupta · International Journal of Apllied Mathematics (2025) | TGRS Research Map | TGRS