scholarly journals Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

2017 ◽  
Vol 161 (11) ◽  
pp. 24-37 ◽  
Author(s):  
Sowmya D. ◽  
P. Deepa ◽  
Venugopal K.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Classification ◽  
Satellite Image ◽  
Image Processing Techniques ◽  
Comprehensive Survey ◽  
Satellite Image Processing ◽  
Remote Sensing Satellite ◽  
Processing Techniques

scholarly journals Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview

2020 ◽  
Vol 10 (12) ◽  
pp. 4207 ◽  
Author(s):  
Anju Asokan ◽  
J. Anitha ◽  
Monica Ciobanu ◽  
Andrei Gabor ◽  
Antoanela Naaji ◽  
...  
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Satellite Image ◽  
Historical Maps ◽  
Satellite Imaging ◽  
Image Processing Techniques ◽  
Innovative Solutions ◽  
Satellite Image Processing ◽  
Processing Techniques ◽  
Selection Of

Historical maps classification has become an important application in today’s scenario of everchanging land boundaries. Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. Hence, an extensive knowledge on satellite image processing is necessary for historical map classification applications. An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper. Though several computational methods are available, different methods perform differently for the various satellite image processing applications. Wrong selection of methods will lead to inferior results for a specific application. This work highlights the methods and the suitable satellite imaging methods associated with these applications. Several comparative analyses are also performed in this work to show the suitability of several methods. This work will help support the selection of innovative solutions for the different problems associated with satellite image processing applications.

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

scholarly journals Optical Remote Sensing

2021 ◽  
pp. 315-344
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Urban Areas ◽  
Satellite Image ◽  
Background Information ◽  
Optical Remote Sensing ◽  
Basic Operation ◽  
City Development ◽  
Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

scholarly journals Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques

2021 ◽  
Vol 6 (3) ◽  
pp. 056-062
Author(s):  
Dena Nadir George ◽  
Haitham Salman Chyad ◽  
Raniah Ali Mustafa
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Medical Imaging ◽  
Image Processing Techniques ◽  
Classification Feature ◽  
Segmentation Methods ◽  
Comprehensive Survey ◽  
Segmentation Image ◽  
Processing Techniques ◽  
Cancer Diseases

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

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