Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas

TL;DR

This paper presents a novel content-based image retrieval system for AWiFS satellite images, focusing on extracting water bodies and burnt areas using dynamic semantic segmentation, with efficient storage and ranking mechanisms.

Contribution

It introduces DSS algorithms for extracting and ranking water and burnt areas from AWiFS images, utilizing sparse feature vectors for efficient storage and retrieval.

Findings

Effective extraction of water bodies and burnt areas achieved.

Significant reduction in computational and storage costs.

System demonstrates efficient retrieval using sparse feature vectors.

Abstract

Satellite Remote Sensing Technology is becoming a major milestone in the prediction of weather anomalies, natural disasters as well as finding alternative resources in proximity using multiple multi-spectral sensors emitting electromagnetic waves at distinct wavelengths. Hence, it is imperative to extract water bodies and burnt areas from orthorectified tiles and correspondingly rank them using similarity measures. Different objects in all the spheres of the earth have the inherent capability of absorbing electromagnetic waves of distant wavelengths. This creates various unique masks in terms of reflectance on the receptor. We propose Dynamic Semantic Segmentation (DSS) algorithms that utilized the mentioned capability to extract and rank Advanced Wide Field Sensor (AWiFS) images according to various features. This system stores data intelligently in the form of a sparse feature vector which drastically mitigates the computational and spatial costs incurred for further analysis. The compressed source image is divided into chunks and stored in the database for quicker retrieval. This work is intended to utilize readily available and cost effective resources like AWiFS dataset instead of depending on advanced technologies like Moderate Resolution Imaging Spectroradiometer (MODIS) for data which is scarce.