Multi-Sensor Systems and Data Fusion in Remote Sensing
Remote sensing is developing rapidly due to progress in many interconnected fields. It includes the emergence of new sensors, development of sophisticated platforms for those sensors, and advances in signal and data processing. The progress in the fields of radar, optoelectronic, acoustic, magnetic,...
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| פורמט: | Online |
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| שפה: | אנגלית |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| נושאים: | |
| גישה מקוונת: | ONIX_20230405_9783036567983_248 |
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| _version_ | 1869521045367554048 |
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| collection | Directory of Open Access Books |
| description | Remote sensing is developing rapidly due to progress in many interconnected fields. It includes the emergence of new sensors, development of sophisticated platforms for those sensors, and advances in signal and data processing. The progress in the fields of radar, optoelectronic, acoustic, magnetic, chemical, and other sensors is stunning. Whereas the mentioned sensors are currently more sensitive and accurate, have improved resolutions, data rates, and dynamical ranges, they still have their limitations. The utilization of multi-sensor systems and joint processing of their signals or data has long been considered an effective solution for reducing the disadvantages and best utilizing their strengths. The emergence of new types of sensors creates an opportunity for scientists and engineers to develop new and more capable integrated multi-sensor systems. It is necessary to mention that the users’ expectations with respect to the size of the observed area or volume, data resolution, accuracy, speed of operation, and functionality of remote sensing systems are still increasing. Extended frequency bands, improved resolutions, and data rates of the new sensors as well as the common use of distributed sensors increase the influx of data in contemporary multi-sensor systems. These facts pose new challenges for the data fusion algorithms that must often employ the newest achievements from the areas of big data mining, statistical estimation, artificial intelligence, etc. This reprint provides a fresh insight into the newest developments in the fields of multi-sensor systems and data fusion. |
| format | Online |
| id | doab-20.500.12854ir-98969 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-989692024-04-11T15:11:04Z Multi-Sensor Systems and Data Fusion in Remote Sensing Kaniewski, Piotr Pasternak, Mateusz Mattoccia, Stefano pansharpening component substitution multiresolution analysis neural networks adaptive weight image registration nonlinear radiation distortions phase congruency multimodal remote sensing image optical and synthetic aperture radar (SAR) phase congruency (PC) radiometric difference INS GPS UAV SAR information quality weather station sensors modelling explosive devices hyperspectral data simulation Spectral Angle Mapping duration calculus data models temporal logic temporal series data fusion data evaluation multisensor data signal and data processing interval logic classification CORINE feature selection LUCAS MDA random forest sentinel infrared and visible image object detection convolutional neural network difference maximum loss function focused feature enhancement module cascaded semantic extension module SLAM autonomous navigation particle filter monocular camera IMU mapping path planning hexagonal grid thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Remote sensing is developing rapidly due to progress in many interconnected fields. It includes the emergence of new sensors, development of sophisticated platforms for those sensors, and advances in signal and data processing. The progress in the fields of radar, optoelectronic, acoustic, magnetic, chemical, and other sensors is stunning. Whereas the mentioned sensors are currently more sensitive and accurate, have improved resolutions, data rates, and dynamical ranges, they still have their limitations. The utilization of multi-sensor systems and joint processing of their signals or data has long been considered an effective solution for reducing the disadvantages and best utilizing their strengths. The emergence of new types of sensors creates an opportunity for scientists and engineers to develop new and more capable integrated multi-sensor systems. It is necessary to mention that the users’ expectations with respect to the size of the observed area or volume, data resolution, accuracy, speed of operation, and functionality of remote sensing systems are still increasing. Extended frequency bands, improved resolutions, and data rates of the new sensors as well as the common use of distributed sensors increase the influx of data in contemporary multi-sensor systems. These facts pose new challenges for the data fusion algorithms that must often employ the newest achievements from the areas of big data mining, statistical estimation, artificial intelligence, etc. This reprint provides a fresh insight into the newest developments in the fields of multi-sensor systems and data fusion. 2023-04-05T13:01:32Z 2023-04-05T13:01:32Z 2023 book ONIX_20230405_9783036567983_248 9783036567983 9783036567990 https://directory.doabooks.org/handle/20.500.12854/98969 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/7029 https://mdpi.com/books/pdfview/book/7029 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6799-0 10.3390/books978-3-0365-6799-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036567983 9783036567990 264 Basel open access |
| spellingShingle | pansharpening component substitution multiresolution analysis neural networks adaptive weight image registration nonlinear radiation distortions phase congruency multimodal remote sensing image optical and synthetic aperture radar (SAR) phase congruency (PC) radiometric difference INS GPS UAV SAR information quality weather station sensors modelling explosive devices hyperspectral data simulation Spectral Angle Mapping duration calculus data models temporal logic temporal series data fusion data evaluation multisensor data signal and data processing interval logic classification CORINE feature selection LUCAS MDA random forest sentinel infrared and visible image object detection convolutional neural network difference maximum loss function focused feature enhancement module cascaded semantic extension module SLAM autonomous navigation particle filter monocular camera IMU mapping path planning hexagonal grid thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Multi-Sensor Systems and Data Fusion in Remote Sensing |
| title | Multi-Sensor Systems and Data Fusion in Remote Sensing |
| title_full | Multi-Sensor Systems and Data Fusion in Remote Sensing |
| title_fullStr | Multi-Sensor Systems and Data Fusion in Remote Sensing |
| title_full_unstemmed | Multi-Sensor Systems and Data Fusion in Remote Sensing |
| title_short | Multi-Sensor Systems and Data Fusion in Remote Sensing |
| title_sort | multi sensor systems and data fusion in remote sensing |
| topic | pansharpening component substitution multiresolution analysis neural networks adaptive weight image registration nonlinear radiation distortions phase congruency multimodal remote sensing image optical and synthetic aperture radar (SAR) phase congruency (PC) radiometric difference INS GPS UAV SAR information quality weather station sensors modelling explosive devices hyperspectral data simulation Spectral Angle Mapping duration calculus data models temporal logic temporal series data fusion data evaluation multisensor data signal and data processing interval logic classification CORINE feature selection LUCAS MDA random forest sentinel infrared and visible image object detection convolutional neural network difference maximum loss function focused feature enhancement module cascaded semantic extension module SLAM autonomous navigation particle filter monocular camera IMU mapping path planning hexagonal grid thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | pansharpening component substitution multiresolution analysis neural networks adaptive weight image registration nonlinear radiation distortions phase congruency multimodal remote sensing image optical and synthetic aperture radar (SAR) phase congruency (PC) radiometric difference INS GPS UAV SAR information quality weather station sensors modelling explosive devices hyperspectral data simulation Spectral Angle Mapping duration calculus data models temporal logic temporal series data fusion data evaluation multisensor data signal and data processing interval logic classification CORINE feature selection LUCAS MDA random forest sentinel infrared and visible image object detection convolutional neural network difference maximum loss function focused feature enhancement module cascaded semantic extension module SLAM autonomous navigation particle filter monocular camera IMU mapping path planning hexagonal grid thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20230405_9783036567983_248 |