Automatic Semantic Segmentation and Classification of Remote Sensing Image Data for Flood Detection Using Novel LSTM Neural Network

Floods are one of the main threats to human life and the property of natural disasters, especially in highly populated urban areas. Fast and accurate extraction of submerged area risky to supporting emergency planning and providing damage assessment in spatial and temporal measurements. Satellite mu...

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Veröffentlicht in:	SN computer science Jg. 5; H. 8; S. 992
Hauptverfasser:	Sonavale, Amruta, Chakkaravarthy, Midhun, Srinivasa Rao, Surampudi, Salleh, Hishamuddin Bin M., Jadhav, Jagannath
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.12.2024 Springer Nature B.V
Schlagworte:	Accuracy Advances in Computational Approaches for Image Processing Algorithms Automation Back propagation Banded structure Binomial distribution Classification Cloud Applications and Network Security Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data analysis Data collection Data Structures and Information Theory Datasets Deep learning Edge detection Error analysis Error detection Floods Image enhancement Image resolution Image segmentation Impact analysis Information Systems and Communication Service Internet of Things Literature reviews Methods Neural networks Original Research Pattern Recognition and Graphics Performance evaluation Pixels Propagation Remote sensing Satellite imagery Saturation (color) Semantic segmentation Semantics Sensors Software Engineering/Programming and Operating Systems Spectrum analysis Statistical methods Technology assessment Threat evaluation Time series Vision Water Wireless Networks Adaptive median filtering Flood detection Neural network algorithm LSTM Versatile Linear Edge Detection (VLED) algorithm Structural object son the RSI
ISSN:	2661-8907, 2662-995X, 2661-8907
Online-Zugang:	Volltext
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