AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA: Flood and damage assessment using very Multi-Temporal-Remote Sensing Images (MT-RSI) data - Couverture souple
Synopsis
Flood detection system process like the four different kinds of preprocessing, segmentation, feature extraction and the Contiguous deep Convolutional neural network (CDCNN) has been executed for identifying the flood defected region. CDCNN the implementation of proposed large-scale data sets can automatically pass through the histological characteristics of several layers of neurons, and has the ability to implement the non-linear decision-making functions. This work also investigates and compare with the possible methods for accurately identified by the classification with the proposed CDCNN details of the RSI. The performance analysis of the proposed model is verified in 2017 B mat lab environment. Based on the different features like precision, recall and F-measure accuracy analysis of the proposed system performance simulation system.
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA
Edité par
LAP LAMBERT Academic Publishing Sep 2020, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Flood detection system process like the four different kinds of preprocessing, segmentation, feature extraction and the Contiguous deep Convolutional neural network (CDCNN) has been executed for identifying the flood defected region. CDCNN the implementation of proposed large-scale data sets can automatically pass through the histological characteristics of several layers of neurons, and has the ability to implement the non-linear decision-making functions. This work also investigates and compare with the possible methods for accurately identified by the classification with the proposed CDCNN details of the RSI. The performance analysis of the proposed model is verified in 2017 B mat lab environment. Based on the different features like precision, recall and F-measure accuracy analysis of the proposed system performance simulation system. 56 pp. Englisch. N° de réf. du vendeur 9786202801010
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA: Flood and damage assessment using very Multi-Temporal-Remote Sensing Images (MT-RSI) data
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
Couverture souple
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA: Flood and damage assessment using very Multi-Temporal-Remote Sensing Images (MT-RSI) data
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
Couverture souple
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA: Flood and damage assessment using very Multi-Temporal-Remote Sensing Images (MT-RSI) data
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
Couverture souple
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
Couverture souple
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jadhav Dr. JagannathDr. Jagannath Jadhav is a Renowned Researcher and Global Scientist holding 3 Patents. He is an academician, researcher, author, writer, inventor and innovator, Scientist (consultant and speaker). He has completed . N° de réf. du vendeur 493983008
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA
Edité par
LAP LAMBERT Academic Publishing Sep 2020, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. Neuware -Flood detection system process like the four different kinds of preprocessing, segmentation, feature extraction and the Contiguous deep Convolutional neural network (CDCNN) has been executed for identifying the flood defected region. CDCNN the implementation of proposed large-scale data sets can automatically pass through the histological characteristics of several layers of neurons, and has the ability to implement the non-linear decision-making functions. This work also investigates and compare with the possible methods for accurately identified by the classification with the proposed CDCNN details of the RSI. The performance analysis of the proposed model is verified in 2017 B mat lab environment. Based on the different features like precision, recall and F-measure accuracy analysis of the proposed system performance simulation system.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch. N° de réf. du vendeur 9786202801010
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AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA : Flood and damage assessment using very Multi-Temporal-Remote Sensing Images (MT-RSI) data
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 6202801018
ISBN 13 : 9786202801010
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Flood detection system process like the four different kinds of preprocessing, segmentation, feature extraction and the Contiguous deep Convolutional neural network (CDCNN) has been executed for identifying the flood defected region. CDCNN the implementation of proposed large-scale data sets can automatically pass through the histological characteristics of several layers of neurons, and has the ability to implement the non-linear decision-making functions. This work also investigates and compare with the possible methods for accurately identified by the classification with the proposed CDCNN details of the RSI. The performance analysis of the proposed model is verified in 2017 B mat lab environment. Based on the different features like precision, recall and F-measure accuracy analysis of the proposed system performance simulation system. N° de réf. du vendeur 9786202801010
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