Smart pansharpening approach using kernel‐based image filtering

Abstract

Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high-frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust spatial data misfortune that happens when images are combined. Experimental results demonstrated that the proposed method provides consistent results concise with reported by the previous research in terms of subjective and objective assessments on remote sensing data. INTRODUCTIONImage fusion is the way toward integrating two or more images into one image. It helps detectable human quality since it joins correlative information of images. Pansharpening consolidates high-spatial panchromatic (PAN) and multispectral (MS) images to create high-resolution multispectral images (HRMS). Remote sensing satellite images are broadly utilised in numerous applications, such as monitoring, metrology, agriculture, planning applications, and military [1, 2], and so forth. Most of the satellite sensors provide high spatial resolution PAN and several MS bands, which lead to difficulty for having a high-resolution multispectral image (HRMS). Along these lines, numerous techniques for the fusion of (MS) and (PAN) images have been proposed. To date, deep learning-based methods provide promising results in many domains, such as image processing, image fusion and computer vision [3, 4]. Recently, various pansharpening techniques have been implemented [5-7] to fuse PAN and MS images to acquire HRMS. The essential part of such strategies is the extraction of the detailed information from the PAN image,This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Metrology, Agriculture and Food: Literature Quantitative Analysis

“…The authors remarked in the conclusion a clear need to develop certified reference materials and reference materials for these beverages for other analytes of interest, such as chlorogenic acids and other phenolic compounds [17]. The most recent "Article" showed the application of a smart pansharpening approach using kernel-based image filtering, as an example of numerous applications of remote sensing image fusion in monitoring, metrology, and agriculture [18]. Another application of innovative technologies is given by Fiorani et al [19] on the use of a photoacoustic laser system for food fraud detection as a reliable technique for the rapid screening of counterfeited ingredients in the supply chain, which needs further development.…”

Study and Analysis of Various Pan-Sharpening Techniques: A Challenging Overview

“…Smadi AA., et al [5] developed a Kernel-based image filtering technique for obtaining the highfrequency information from PAN image. In this method, different kinds of side window-guided filters were utilized for improving the MS band from the PAN image.…”

“…Following are the various research problems faced by the other approaches and are illustrated given as follows: In [1], the introduced approach failed to incorporate various pan-sharpening techniques for maximizing the overall available choices for the user, and the accuracy-based evaluation. The proposed approach in [5] failed to consider the local linear filters in order to enhance the image fusion process. The newly designed technique in [6] failed to resolve the computational complexity issues.…”

Deep convolutional neural network-based system for fish classification

“…In recent years, computer sciences and technology have played a key role in many areas, such as the internet of things [1], network security [2], object detection, scene classification [3], and remote sensing [4]. Scene classification plays a key role in daily life due to alteration in the scenes' countenance and environment.…”